檢查 Google Cloud 儲存空間和資料庫，找出機密資料

妥善管理儲存在儲存空間存放區的機密資料，首先要進行儲存空間分類：找出存放區中的機密資料位置、類型和使用方式。這項知識有助於您正確設定存取控管和共用權限，並可納入持續監控計畫。

Sensitive Data Protection 可偵測及分類儲存在 Cloud Storage 位置、Datastore 種類或 BigQuery 資料表中的機密資料。掃描 Cloud Storage 位置中的檔案時，Sensitive Data Protection 支援掃描二進位、文字、圖片、Microsoft Word、Microsoft Excel、Microsoft PowerPoint、PDF 和 Apache Avro 檔案。系統會將無法辨識的檔案類型視為二進位檔案掃描。如要進一步瞭解支援的檔案類型，請參閱「支援的檔案類型」。

如要檢查儲存空間和資料庫中的機密資料，請指定資料位置，以及 Sensitive Data Protection 應尋找的機密資料類型。Sensitive Data Protection 會啟動工作，檢查指定位置的資料，然後提供在內容中找到的 infoTypes、可能性值等詳細資料。

您可以使用 Google Cloud 控制台中的 Sensitive Data Protection，透過 RESTful DLP API，或以程式輔助方式，使用多種語言的 Sensitive Data Protection 用戶端程式庫，設定儲存空間和資料庫的檢查作業。

本主題包括以下內容：

• 設定掃描 Google Cloud 儲存空間存放區和資料庫的最佳做法。
• 操作說明：在 Google Cloud 控制台使用 Sensitive Data Protection 設定檢查掃描作業，以及排定定期重複檢查掃描作業 (選用)。
• 各儲存空間存放區類型的 JSON 和程式碼範例：(Cloud Storage、Datastore 模式的 Firestore (Datastore) 和 BigQuery)。 Google Cloud
• 掃描工作的設定選項詳細總覽。
• 說明如何擷取掃描結果，以及如何管理透過每項成功要求建立的掃描工作。

最佳做法

找出並優先掃描

請務必先評估資產，並指定哪些資產的掃描優先順序最高。剛開始時，您可能會有大量待分類的資料，無法立即全部掃描。一開始請選擇潛在風險最高的資料，例如經常存取、可供廣泛存取或不明的資料。

確認 Sensitive Data Protection 能存取您的資料

Sensitive Data Protection 必須能存取要掃描的資料。請確認 Sensitive Data Protection 服務帳戶有權讀取您的資源。

限制首次掃描的範圍

為獲得最佳結果，請限制第一項工作的範圍，不要掃描所有資料。先從一個資料表、一個 bucket 或幾個檔案開始，並使用取樣功能。限制首次掃描的範圍，有助於判斷要啟用哪些偵測器，以及可能需要哪些排除規則來減少偽陽性，讓結果更有意義。如果不需要所有 infoType，請避免全部啟用，因為偽陽性或無法使用的發現項目可能會增加評估風險的難度。雖然 DATE、TIME、DOMAIN_NAME 和 URL 等 infoType 在某些情況下很有用，但這類 infoType 會比對廣泛的發現項目，因此可能不適合用於大型資料掃描。

對結構化檔案 (例如 CSV、TSV 或 Avro 檔案) 進行取樣時，請確保樣本大小足以涵蓋檔案的完整標頭和一列資料。詳情請參閱「在結構化剖析模式中掃描結構化檔案」。

安排掃描時間

使用 Sensitive Data Protection 作業觸發條件，每天、每週或每季自動執行掃描並產生結果。您也可以設定這些掃描作業，只檢查上次掃描後變更的資料，這樣就能節省時間並降低成本。定期執行掃描，有助於找出掃描結果的趨勢或異常狀況。

工作延遲時間

我們不保證作業和作業觸發程序會達到任何服務水準目標 (SLO)。延遲時間會受到多種因素影響，包括要掃描的資料量、掃描的儲存空間存放區、要掃描的 infoType 類型和數量、處理作業的區域，以及該區域可用的運算資源。因此，檢查工作的延遲時間無法事先確定。

如要縮短工作延遲時間，可以嘗試下列做法：

• 如果工作或工作觸發條件支援取樣功能，請啟用。

• 請避免啟用不需要的 infoType。雖然下列資訊類型在特定情境中很有用，但與不含這些資訊類型的要求相比，這些資訊類型可能會導致要求執行速度慢上許多：

  • PERSON_NAME
  • FEMALE_NAME
  • MALE_NAME
  • FIRST_NAME
  • LAST_NAME
  • DATE_OF_BIRTH
  • LOCATION
  • STREET_ADDRESS
  • ORGANIZATION_NAME

• 請務必明確指定 infoType。請勿使用空白的 infoTypes 清單。

• 請盡可能改用其他處理區域。

如果嘗試這些技術後，工作仍有延遲問題，請考慮使用 content.inspect 或 content.deidentify 要求，而非工作。這些方法適用服務水準協議。詳情請參閱「Sensitive Data Protection 服務水準協議」。

事前準備

本主題提供的操作說明假設如下：

• 您已啟用計費功能。

  瞭解如何啟用計費功能

• 您已啟用 Sensitive Data Protection。

  啟用 Sensitive Data Protection

儲存空間分類需要下列 OAuth 範圍： https://www.googleapis.com/auth/cloud-platform。詳情請參閱「向 DLP API 進行驗證」。

檢查 Cloud Storage 位置

您可以透過 Google Cloud 控制台、透過 REST 或 RPC 要求使用 DLP API，或以多種語言透過用戶端程式庫，設定 Cloud Storage 位置的 Sensitive Data Protection 檢查。如要瞭解下列 JSON 和程式碼範例中包含的參數，請參閱本主題稍後的「設定儲存空間檢查」一節。

Sensitive Data Protection 會根據檔案副檔名和媒體 (MIME) 類型，判斷要掃描的檔案類型，以及要套用的掃描模式。舉例來說，Sensitive Data Protection 會以純文字模式掃描 .txt 檔案，即使檔案結構為 CSV 檔案 (通常以結構化剖析模式掃描) 也是如此。

注意事項：如果您在工作執行期間提前取消作業，系統仍會按照已完成的工作比例向您收取費用。如要進一步瞭解帳單，請參閱「Sensitive Data Protection 定價」。

注意： Sensitive Data Protection 只會掃描工作啟動時的檔案版本。如果停用 Cloud Storage 版本控管，且檔案在工作啟動後但完成前經過修改，系統會略過該檔案。否則 Sensitive Data Protection 會掃描舊版檔案。如要為 Cloud Storage 物件啟用版本管理功能，請參閱 Cloud Storage 說明文件。

重要事項： 如要使用本頁面的程式碼，請先設定 Sensitive Data Protection 用戶端。如要進一步瞭解如何安裝及建立 Sensitive Data Protection 用戶端，請參閱「Sensitive Data Protection 用戶端程式庫」。(將 JSON 傳送至 Sensitive Data Protection REST 端點時，不需要用戶端程式庫)。

如要使用 Sensitive Data Protection 設定 Cloud Storage bucket 的掃描工作，請按照下列步驟操作：

控制台

本節說明如何檢查 Cloud Storage bucket 或資料夾。如要讓 Sensitive Data Protection 建立去識別化的資料副本，請參閱「使用 Google Cloud 控制台將儲存在 Cloud Storage 的機密資料去識別化」。

1. 在 Google Cloud 控制台的「Sensitive Data Protection」專區中，前往「Create job or job trigger」(建立工作或工作觸發條件) 頁面。

   前往「建立工作或工作觸發條件」

   注意： 您也可以直接從 Cloud Storage 瀏覽器啟動 Sensitive Data Protection 掃描。在要掃描的 bucket 最右側欄中，按一下「more actions」(更多動作) 選單 (顯示為垂直排列的三個點) ，然後按一下「Scan with Sensitive Data Protection」(使用 Sensitive Data Protection 掃描)。Sensitive Data Protection 工作建立頁面會在另一個分頁開啟。

2. 輸入 Sensitive Data Protection 工作資訊，然後點選「Continue」(繼續)，完成各個步驟：

   • 在「選擇輸入資料」部分，在「名稱」欄位中輸入值，為工作命名。在「位置」中，從「儲存空間類型」選單選擇 Cloud Storage，然後輸入要掃描的資料位置。 「取樣」部分已預先設定，可對您的資料執行範例掃描。如果您有大量資料，可以調整「Percentage of objects scanned within bucket」(在 bucket 中掃描的物件百分比) 欄位，節省資源。詳情請參閱「選擇輸入資料」。

   • (選用) 在「設定偵測作業」部分，您可以設定要尋找的資料類型，稱為「infoTypes」。您可以從預先定義的 infoType 清單中選取，也可以選取現有範本。詳情請參閱「設定偵測功能」。

   • 在「新增動作」部分，選取一或多個動作，讓 Sensitive Data Protection 功能在工作完成後執行。詳情請參閱「啟用檢查或風險分析動作」。

     選取動作後，按一下「繼續」。

   • (選用) 如要排程，但只想執行一次掃描，請將選單設為「無」。如要排定定期執行掃描作業，請按一下「Create a trigger to run the job on a periodic schedule」(建立觸發條件來定期執行工作)。詳情請參閱「排程」。

3. 點選「建立」。

4. Sensitive Data Protection 工作完成後，系統會將您重新導向至工作詳細資料頁面，並透過電子郵件通知您。您可以在工作詳細資料頁面查看檢查結果。

5. (選用) 如果您選擇將 Sensitive Data Protection 發現項目發布至 BigQuery，請在「Job details」(工作詳細資料) 頁面中，按一下「View Findings in BigQuery」(在 BigQuery 中查看發現項目)，在 BigQuery 網頁版 UI 中開啟資料表。接著，您可以查詢資料表並分析結果。如要進一步瞭解如何在 BigQuery 中查詢結果，請參閱「在 BigQuery 中查詢 Sensitive Data Protection 發現項目」。

通訊協定

以下是 JSON 範例，可透過 POST 要求傳送至指定的 Sensitive Data Protection REST 端點。這個 JSON 範例示範如何使用 DLP API 檢查 Cloud Storage 值區。如要瞭解要求中包含的參數，請參閱本主題後續的「設定儲存空間檢查」一節。

注意： 如要指定要掃描的 Cloud Storage bucket，請使用下列格式輸入網址：gs://[GCS_BUCKET_NAME]/*。如要只掃描指定目錄，不掃描更深層的目錄，請使用一個星號 (*)。如要遞迴掃描目錄，請使用兩個星號 (**)。

您可以在 content.inspect 的參考頁面上，透過 APIs Explorer 快速試用這項功能：

前往 APIs Explorer

請注意，即使是在 APIs Explorer 中，成功的要求也會建立新的掃描工作。如要瞭解如何控管掃描作業，請參閱本主題稍後的「擷取檢查結果」。如要瞭解如何使用 JSON 向 DLP API 傳送要求，請參閱 JSON 快速入門導覽課程。

JSON 輸入：

POST https://dlp.googleapis.com/v2/projects/[PROJECT-ID]/dlpJobs?key={YOUR_API_KEY}

{
  "inspectJob":{
    "storageConfig":{
      "cloudStorageOptions":{
        "fileSet":{
          "url":"gs://[BUCKET-NAME]/*"
        },
        "bytesLimitPerFile":"1073741824"
      },
      "timespanConfig":{
        "startTime":"2017-11-13T12:34:29.965633345Z",
        "endTime":"2018-01-05T04:45:04.240912125Z"
      }
    },
    "inspectConfig":{
      "infoTypes":[
        {
          "name":"PHONE_NUMBER"
        }
      ],
      "excludeInfoTypes":false,
      "includeQuote":true,
      "minLikelihood":"LIKELY"
    },
    "actions":[
      {
        "saveFindings":{
          "outputConfig":{
            "table":{
              "projectId":"[PROJECT-ID]",
              "datasetId":"[DATASET-ID]"
            }
          }
        }
      }
    ]
  }
}

JSON 輸出內容：

{
  "name":"projects/[PROJECT-ID]/dlpJobs/[JOB-ID]",
  "type":"INSPECT_JOB",
  "state":"PENDING",
  "inspectDetails":{
    "requestedOptions":{
      "snapshotInspectTemplate":{

      },
      "jobConfig":{
        "storageConfig":{
          "cloudStorageOptions":{
            "fileSet":{
              "url":"gs://[BUCKET-NAME]/*"
            },
            "bytesLimitPerFile":"1073741824"
          },
          "timespanConfig":{
            "startTime":"2017-11-13T12:34:29.965633345Z",
            "endTime":"2018-01-05T04:45:04.240912125Z"
          }
        },
        "inspectConfig":{
          "infoTypes":[
            {
              "name":"PHONE_NUMBER"
            }
          ],
          "minLikelihood":"LIKELY",
          "limits":{

          },
          "includeQuote":true
        },
        "actions":[
          {
            "saveFindings":{
              "outputConfig":{
                "table":{
                  "projectId":"[PROJECT-ID]",
                  "datasetId":"[DATASET-ID]",
                  "tableId":"[NEW-TABLE-ID]"
                }
              }
            }
          }
        ]
      }
    }
  },
  "createTime":"2018-11-07T18:01:14.225Z"
}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.CloudStorageOptions;
import com.google.privacy.dlp.v2.CloudStorageOptions.FileSet;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InfoTypeStats;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectDataSourceDetails;
import com.google.privacy.dlp.v2.InspectJobConfig;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.StorageConfig;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.stream.Collectors;
import java.util.stream.Stream;

public class InspectGcsFile {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String gcsUri = "gs://" + "your-bucket-name" + "/path/to/your/file.txt";
    String topicId = "your-pubsub-topic-id";
    String subscriptionId = "your-pubsub-subscription-id";
    inspectGcsFile(projectId, gcsUri, topicId, subscriptionId);
  }

  // Inspects a file in a Google Cloud Storage Bucket.
  public static void inspectGcsFile(
      String projectId, String gcsUri, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlp = DlpServiceClient.create()) {
      // Specify the GCS file to be inspected.
      CloudStorageOptions cloudStorageOptions =
          CloudStorageOptions.newBuilder().setFileSet(FileSet.newBuilder().setUrl(gcsUri)).build();

      StorageConfig storageConfig =
          StorageConfig.newBuilder().setCloudStorageOptions(cloudStorageOptions).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      List<InfoType> infoTypes =
          Stream.of("PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD_NUMBER")
              .map(it -> InfoType.newBuilder().setName(it).build())
              .collect(Collectors.toList());

      // Specify how the content should be inspected.
      InspectConfig inspectConfig =
          InspectConfig.newBuilder().addAllInfoTypes(infoTypes).setIncludeQuote(true).build();

      // Specify the action that is triggered when the job completes.
      String pubSubTopic = String.format("projects/%s/topics/%s", projectId, topicId);
      Action.PublishToPubSub publishToPubSub =
          Action.PublishToPubSub.newBuilder().setTopic(pubSubTopic).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the long running job we want the service to perform.
      InspectJobConfig inspectJobConfig =
          InspectJobConfig.newBuilder()
              .setStorageConfig(storageConfig)
              .setInspectConfig(inspectConfig)
              .addActions(action)
              .build();

      // Create the request for the job configured above.
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setInspectJob(inspectJobConfig)
              .build();

      // Use the client to send the request.
      final DlpJob dlpJob = dlp.createDlpJob(createDlpJobRequest);
      System.out.println("Job created: " + dlpJob.getName());

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Get the latest state of the job from the service
      GetDlpJobRequest request = GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();
      DlpJob completedJob = dlp.getDlpJob(request);

      // Parse the response and process results.
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());
      InspectDataSourceDetails.Result result = completedJob.getInspectDetails().getResult();
      System.out.println("Findings: ");
      for (InfoTypeStats infoTypeStat : result.getInfoTypeStatsList()) {
        System.out.print("\tInfo type: " + infoTypeStat.getInfoType().getName());
        System.out.println("\tCount: " + infoTypeStat.getCount());
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The name of the bucket where the file resides.
// const bucketName = 'YOUR-BUCKET';

// The path to the file within the bucket to inspect.
// Can contain wildcards, e.g. "my-image.*"
// const fileName = 'my-image.png';

// The minimum likelihood required before returning a match
// const minLikelihood = 'LIKELIHOOD_UNSPECIFIED';

// The maximum number of findings to report per request (0 = server maximum)
// const maxFindings = 0;

// The infoTypes of information to match
// const infoTypes = [{ name: 'PHONE_NUMBER' }, { name: 'EMAIL_ADDRESS' }, { name: 'CREDIT_CARD_NUMBER' }];

// The customInfoTypes of information to match
// const customInfoTypes = [{ infoType: { name: 'DICT_TYPE' }, dictionary: { wordList: { words: ['foo', 'bar', 'baz']}}},
//   { infoType: { name: 'REGEX_TYPE' }, regex: {pattern: '\\(\\d{3}\\) \\d{3}-\\d{4}'}}];

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

async function inspectGCSFile() {
  // Get reference to the file to be inspected
  const storageItem = {
    cloudStorageOptions: {
      fileSet: {url: `gs://${bucketName}/${fileName}`},
    },
  };

  // Construct request for creating an inspect job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectJob: {
      inspectConfig: {
        infoTypes: infoTypes,
        customInfoTypes: customInfoTypes,
        minLikelihood: minLikelihood,
        limits: {
          maxFindingsPerRequest: maxFindings,
        },
      },
      storageConfig: storageItem,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Create a GCS File inspection job and wait for it to complete
  const [topicResponse] = await pubsub.topic(topicId).get();
  // Verify the Pub/Sub topic and listen for job notifications via an
  // existing subscription.
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  // Get the job's ID
  const jobName = jobsResponse.name;
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });

  setTimeout(() => {
    console.log('Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  console.log(`Job ${job.name} status: ${job.state}`);

  const infoTypeStats = job.inspectDetails.result.infoTypeStats;
  if (infoTypeStats.length > 0) {
    infoTypeStats.forEach(infoTypeStat => {
      console.log(
        `  Found ${infoTypeStat.count} instance(s) of infoType ${infoTypeStat.infoType.name}.`
      );
    });
  } else {
    console.log('No findings.');
  }
}
await inspectGCSFile();

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import threading
from typing import List, Optional

import google.cloud.dlp
import google.cloud.pubsub


def inspect_gcs_file(
    project: str,
    bucket: str,
    filename: str,
    topic_id: str,
    subscription_id: str,
    info_types: List[str],
    custom_dictionaries: List[str] = None,
    custom_regexes: List[str] = None,
    min_likelihood: Optional[str] = None,
    max_findings: Optional[int] = None,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to analyze a file on GCS.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        bucket: The name of the GCS bucket containing the file, as a string.
        filename: The name of the file in the bucket, including the path, as a
            string; e.g. 'images/myfile.png'.
        topic_id: The id of the Cloud Pub/Sub topic to which the API will
            broadcast job completion. The topic must already exist.
        subscription_id: The id of the Cloud Pub/Sub subscription to listen on
            while waiting for job completion. The subscription must already
            exist and be subscribed to the topic.
        info_types: A list of strings representing info types to look for.
            A full list of info type categories can be fetched from the API.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        timeout: The number of seconds to wait for a response from the API.
    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries (protos are also accepted).
    if not info_types:
        info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
    info_types = [{"name": info_type} for info_type in info_types]

    # Prepare custom_info_types by parsing the dictionary word lists and
    # regex patterns.
    if custom_dictionaries is None:
        custom_dictionaries = []
    dictionaries = [
        {
            "info_type": {"name": f"CUSTOM_DICTIONARY_{i}"},
            "dictionary": {"word_list": {"words": custom_dict.split(",")}},
        }
        for i, custom_dict in enumerate(custom_dictionaries)
    ]
    if custom_regexes is None:
        custom_regexes = []
    regexes = [
        {
            "info_type": {"name": f"CUSTOM_REGEX_{i}"},
            "regex": {"pattern": custom_regex},
        }
        for i, custom_regex in enumerate(custom_regexes)
    ]
    custom_info_types = dictionaries + regexes

    # Construct the configuration dictionary. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": info_types,
        "custom_info_types": custom_info_types,
        "min_likelihood": min_likelihood,
        "limits": {"max_findings_per_request": max_findings},
    }

    # Construct a storage_config containing the file's URL.
    url = f"gs://{bucket}/{filename}"
    storage_config = {"cloud_storage_options": {"file_set": {"url": url}}}

    # Convert the project id into full resource ids.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Construct the inspect_job, which defines the entire inspect content task.
    inspect_job = {
        "inspect_config": inspect_config,
        "storage_config": storage_config,
        "actions": actions,
    }

    operation = dlp.create_dlp_job(
        request={"parent": parent, "inspect_job": inspect_job}
    )
    print(f"Inspection operation started: {operation.name}")

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)

    # Set up a callback to acknowledge a message. This closes around an event
    # so that it can signal that it is done and the main thread can continue.
    job_done = threading.Event()

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        try:
            if message.attributes["DlpJobName"] == operation.name:
                # This is the message we're looking for, so acknowledge it.
                message.ack()

                # Now that the job is done, fetch the results and print them.
                job = dlp.get_dlp_job(request={"name": operation.name})
                print(f"Job name: {job.name}")
                if job.inspect_details.result.info_type_stats:
                    for finding in job.inspect_details.result.info_type_stats:
                        print(
                            f"Info type: {finding.info_type.name}; Count: {finding.count}"
                        )
                else:
                    print("No findings.")

                # Signal to the main thread that we can exit.
                job_done.set()
            else:
                # This is not the message we're looking for.
                message.drop()
        except Exception as e:
            # Because this is executing in a thread, an exception won't be
            # noted unless we print it manually.
            print(e)
            raise

    subscriber.subscribe(subscription_path, callback=callback)
    finished = job_done.wait(timeout=timeout)
    if not finished:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// inspectGCSFile searches for the given info types in the given file.
func inspectGCSFile(w io.Writer, projectID string, infoTypeNames []string, customDictionaries []string, customRegexes []string, pubSubTopic, pubSubSub, bucketName, fileName string) error {
	// projectID := "my-project-id"
	// infoTypeNames := []string{"US_SOCIAL_SECURITY_NUMBER"}
	// customDictionaries := []string{...}
	// customRegexes := []string{...}
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// bucketName := "my-bucket"
	// fileName := "my-file.txt"

	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Convert the info type strings to a list of InfoTypes.
	var infoTypes []*dlppb.InfoType
	for _, it := range infoTypeNames {
		infoTypes = append(infoTypes, &dlppb.InfoType{Name: it})
	}
	// Convert the custom dictionary word lists and custom regexes to a list of CustomInfoTypes.
	var customInfoTypes []*dlppb.CustomInfoType
	for idx, it := range customDictionaries {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_DICTIONARY_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Dictionary_{
				Dictionary: &dlppb.CustomInfoType_Dictionary{
					Source: &dlppb.CustomInfoType_Dictionary_WordList_{
						WordList: &dlppb.CustomInfoType_Dictionary_WordList{
							Words: strings.Split(it, ","),
						},
					},
				},
			},
		})
	}
	for idx, it := range customRegexes {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_REGEX_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Regex_{
				Regex: &dlppb.CustomInfoType_Regex{
					Pattern: it,
				},
			},
		})
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("pubsub.NewClient: %w", err)
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	if exists, err := t.Exists(ctx); err != nil {
		return fmt.Errorf("t.Exists: %w", err)
	} else if !exists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return fmt.Errorf("CreateTopic: %w", err)
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	if exists, err := s.Exists(ctx); err != nil {
		return fmt.Errorf("s.Exists: %w", err)
	} else if !exists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return fmt.Errorf("CreateSubscription: %w", err)
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_InspectJob{
			InspectJob: &dlppb.InspectJobConfig{
				// StorageConfig describes where to find the data.
				StorageConfig: &dlppb.StorageConfig{
					Type: &dlppb.StorageConfig_CloudStorageOptions{
						CloudStorageOptions: &dlppb.CloudStorageOptions{
							FileSet: &dlppb.CloudStorageOptions_FileSet{
								Url: "gs://" + bucketName + "/" + fileName,
							},
						},
					},
				},
				// InspectConfig describes what fields to look for.
				InspectConfig: &dlppb.InspectConfig{
					InfoTypes:       infoTypes,
					CustomInfoTypes: customInfoTypes,
					MinLikelihood:   dlppb.Likelihood_POSSIBLE,
					Limits: &dlppb.InspectConfig_FindingLimits{
						MaxFindingsPerRequest: 10,
					},
					IncludeQuote: true,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the inspect job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the inspect job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()

		// Stop listening for more messages.
		defer cancel()

		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "Cloud not get job: %v", err)
			return
		}
		r := resp.GetInspectDetails().GetResult().GetInfoTypeStats()
		if len(r) == 0 {
			fmt.Fprintf(w, "No results")
		}
		for _, s := range r {
			fmt.Fprintf(w, "  Found %v instances of infoType %v\n", s.GetCount(), s.GetInfoType().GetName())
		}
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CloudStorageOptions;
use Google\Cloud\Dlp\V2\CloudStorageOptions\FileSet;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectConfig\FindingLimits;
use Google\Cloud\Dlp\V2\InspectJobConfig;
use Google\Cloud\Dlp\V2\Likelihood;
use Google\Cloud\Dlp\V2\StorageConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Inspect a file stored on Google Cloud Storage , using Pub/Sub for job status notifications.
 *
 * @param string $callingProjectId  The project ID to run the API call under
 * @param string $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string $bucketId          The name of the bucket where the file resides
 * @param string $file              The path to the file within the bucket to inspect. Can contain wildcards e.g. "my-image.*"
 * @param int    $maxFindings       (Optional) The maximum number of findings to report per request (0 = server maximum)
 */
function inspect_gcs(
    string $callingProjectId,
    string $topicId,
    string $subscriptionId,
    string $bucketId,
    string $file,
    int $maxFindings = 0
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // The infoTypes of information to match
    $personNameInfoType = (new InfoType())
        ->setName('PERSON_NAME');
    $creditCardNumberInfoType = (new InfoType())
        ->setName('CREDIT_CARD_NUMBER');
    $infoTypes = [$personNameInfoType, $creditCardNumberInfoType];

    // The minimum likelihood required before returning a match
    $minLikelihood = likelihood::LIKELIHOOD_UNSPECIFIED;

    // Specify finding limits
    $limits = (new FindingLimits())
        ->setMaxFindingsPerRequest($maxFindings);

    // Construct items to be inspected
    $fileSet = (new FileSet())
        ->setUrl('gs://' . $bucketId . '/' . $file);

    $cloudStorageOptions = (new CloudStorageOptions())
        ->setFileSet($fileSet);

    $storageConfig = (new StorageConfig())
        ->setCloudStorageOptions($cloudStorageOptions);

    // Construct the inspect config object
    $inspectConfig = (new InspectConfig())
        ->setMinLikelihood($minLikelihood)
        ->setLimits($limits)
        ->setInfoTypes($infoTypes);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct inspect job config to run
    $inspectJob = (new InspectJobConfig())
        ->setInspectConfig($inspectConfig)
        ->setStorageConfig($storageConfig)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setInspectJob($inspectJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $infoTypeStats = $job->getInspectDetails()->getResult()->getInfoTypeStats();
            if (count($infoTypeStats) === 0) {
                print('No findings.' . PHP_EOL);
            } else {
                foreach ($infoTypeStats as $infoTypeStat) {
                    printf('  Found %s instance(s) of infoType %s' . PHP_EOL, $infoTypeStat->getCount(), $infoTypeStat->getInfoType()->getName());
                }
            }
            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using System;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.InspectConfig.Types;

public class InspectGoogleCloudStorage
{
    public static DlpJob InspectGCS(
        string projectId,
        Likelihood minLikelihood,
        int maxFindings,
        bool includeQuote,
        IEnumerable<InfoType> infoTypes,
        IEnumerable<CustomInfoType> customInfoTypes,
        string bucketName,
        string topicId,
        string subscriptionId)
    {
        var inspectJob = new InspectJobConfig
        {
            StorageConfig = new StorageConfig
            {
                CloudStorageOptions = new CloudStorageOptions
                {
                    FileSet = new CloudStorageOptions.Types.FileSet { Url = $"gs://{bucketName}/*.txt" },
                    BytesLimitPerFile = 1073741824
                },
            },
            InspectConfig = new InspectConfig
            {
                InfoTypes = { infoTypes },
                CustomInfoTypes = { customInfoTypes },
                ExcludeInfoTypes = false,
                IncludeQuote = includeQuote,
                Limits = new FindingLimits
                {
                    MaxFindingsPerRequest = maxFindings
                },
                MinLikelihood = minLikelihood
            },
            Actions =
                {
                    new Google.Cloud.Dlp.V2.Action
                    {
                        // Send results to Pub/Sub topic
                        PubSub = new Google.Cloud.Dlp.V2.Action.Types.PublishToPubSub
                        {
                            Topic = topicId,
                        }
                    }
                }
        };

        // Issue Create Dlp Job Request
        var client = DlpServiceClient.Create();
        var request = new CreateDlpJobRequest
        {
            InspectJob = inspectJob,
            Parent = new LocationName(projectId, "global").ToString(),
        };

        // We need created job name
        var dlpJob = client.CreateDlpJob(request);

        // Get a pub/sub subscription and listen for DLP results
        var fireEvent = new ManualResetEventSlim();

        var subscriptionName = new SubscriptionName(projectId, subscriptionId);
        var subscriber = SubscriberClient.CreateAsync(subscriptionName).Result;
        subscriber.StartAsync(
            (pubSubMessage, cancellationToken) =>
            {
                // Given a message that we receive on this subscription, we should either acknowledge or decline it
                if (pubSubMessage.Attributes["DlpJobName"] == dlpJob.Name)
                {
                    fireEvent.Set();
                    return Task.FromResult(SubscriberClient.Reply.Ack);
                }

                return Task.FromResult(SubscriberClient.Reply.Nack);
            });

        // We block here until receiving a signal from a separate thread that is waiting on a message indicating receiving a result of Dlp job
        if (fireEvent.Wait(TimeSpan.FromMinutes(1)))
        {
            // Stop the thread that is listening to messages as a result of StartAsync call earlier
            subscriber.StopAsync(CancellationToken.None).Wait();

            // Now we can inspect full job results
            var job = client.GetDlpJob(new GetDlpJobRequest { DlpJobName = new DlpJobName(projectId, dlpJob.Name) });

            // Inspect Job details
            Console.WriteLine($"Processed bytes: {job.InspectDetails.Result.ProcessedBytes}");
            Console.WriteLine($"Total estimated bytes: {job.InspectDetails.Result.TotalEstimatedBytes}");
            var stats = job.InspectDetails.Result.InfoTypeStats;
            Console.WriteLine("Found stats:");
            foreach (var stat in stats)
            {
                Console.WriteLine($"{stat.InfoType.Name}");
            }

            return job;
        }

        throw new InvalidOperationException("The wait failed on timeout");
    }
}

檢查 Datastore 種類

您可以透過Google Cloud 控制台、透過 REST 或 RPC 要求使用 DLP API，或以多種語言透過用戶端程式庫，設定檢查 Datastore 種類。

注意： 如要瞭解下列 JSON 和程式碼範例中包含的參數，請參閱本主題稍後的「設定儲存空間檢查」。

重要事項： 如要使用本頁面的程式碼，請先設定 Sensitive Data Protection 用戶端。如要進一步瞭解如何安裝及建立 Sensitive Data Protection 用戶端，請參閱「Sensitive Data Protection 用戶端程式庫」。(將 JSON 傳送至 Sensitive Data Protection REST 端點時，不需要用戶端程式庫)。

如要使用 Sensitive Data Protection 設定 Datastore 類型的掃描工作，請按照下列步驟操作：

控制台

如要使用 Sensitive Data Protection 設定 Datastore 類型的掃描工作，請按照下列步驟操作：

1. 在 Google Cloud 控制台的「Sensitive Data Protection」部分，前往「Create job or job trigger」(建立工作或工作觸發條件) 頁面。

   前往「Create job or job trigger」(建立工作或工作觸發條件) 頁面

2. 輸入 Sensitive Data Protection 工作資訊，然後按一下「繼續」，完成下列步驟：

   • 在「Choose input data」(選擇輸入資料) 部分，輸入要掃描的專案、命名空間 (選用) 和種類 ID。詳情請參閱「選擇輸入資料」。

   • (選用) 在「設定偵測作業」部分，您可以設定要尋找的資料類型，也就是「infoTypes」。您可以從預先定義的 infoType 清單中選取，也可以選取現有範本。詳情請參閱「設定偵測功能」。

   • 在「Add actions」(新增動作) 部分，選取一或多個動作，讓 Sensitive Data Protection 在工作完成後執行。詳情請參閱「啟用檢查或風險分析動作」。

     選取動作後，按一下「繼續」。

   • (選用) 在「時間表」部分，選取「指定時間範圍」或「建立觸發條件來定期執行工作」，設定時間範圍或時間表。詳情請參閱「時間表」。

3. 點選「建立」。

4. Sensitive Data Protection 工作完成後，系統會將您重新導向至工作詳細資料頁面，並透過電子郵件通知您。您可以在工作詳細資料頁面查看檢查結果。

5. (選用) 如果您選擇將 Sensitive Data Protection 發現結果發布至 BigQuery，請在「工作詳細資料」頁面中，按一下「在 BigQuery 中查看發現結果」，在 BigQuery 網頁版 UI 中開啟資料表。接著，您可以查詢資料表並分析結果。如要進一步瞭解如何在 BigQuery 中查詢結果，請參閱「查詢 Sensitive Data Protection 在 BigQuery 的發現結果」。

通訊協定

以下是 JSON 範例，可透過 POST 要求傳送至指定的 DLP API REST 端點。這個 JSON 範例說明如何使用 DLP API 檢查 Datastore 種類。如要瞭解要求中包含的參數，請參閱本主題稍後的「設定儲存空間檢查」一節。

您可以在 dlpJobs.create 的參考頁面上，透過 APIs Explorer 快速試用這項功能：

前往 APIs Explorer

請注意，即使是在 APIs Explorer 中，只要要求成功，就會建立新的掃描工作。如要瞭解如何控管掃描工作，請參閱本主題後續的「擷取檢查結果」一節。如要瞭解如何使用 JSON 將要求傳送至 DLP API，請參閱 JSON 快速入門指南。

JSON 輸入：

POST https://dlp.googleapis.com/v2/projects/[PROJECT-ID]/dlpJobs?key={YOUR_API_KEY}

{
  "inspectJob":{
    "storageConfig":{
      "datastoreOptions":{
        "kind":{
          "name":"Example-Kind"
        },
        "partitionId":{
          "namespaceId":"[NAMESPACE-ID]",
          "projectId":"[PROJECT-ID]"
        }
      }
    },
    "inspectConfig":{
      "infoTypes":[
        {
          "name":"PHONE_NUMBER"
        }
      ],
      "excludeInfoTypes":false,
      "includeQuote":true,
      "minLikelihood":"LIKELY"
    },
    "actions":[
      {
        "saveFindings":{
          "outputConfig":{
            "table":{
              "projectId":"[PROJECT-ID]",
              "datasetId":"[BIGQUERY-DATASET-NAME]",
              "tableId":"[BIGQUERY-TABLE-NAME]"
            }
          }
        }
      }
    ]
  }
}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DatastoreOptions;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InfoTypeStats;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectDataSourceDetails;
import com.google.privacy.dlp.v2.InspectJobConfig;
import com.google.privacy.dlp.v2.KindExpression;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PartitionId;
import com.google.privacy.dlp.v2.StorageConfig;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.stream.Collectors;
import java.util.stream.Stream;

public class InspectDatastoreEntity {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String datastoreNamespace = "your-datastore-namespace";
    String datastoreKind = "your-datastore-kind";
    String topicId = "your-pubsub-topic-id";
    String subscriptionId = "your-pubsub-subscription-id";
    insepctDatastoreEntity(projectId, datastoreNamespace, datastoreKind, topicId, subscriptionId);
  }

  // Inspects a Datastore Entity.
  public static void insepctDatastoreEntity(
      String projectId,
      String datastoreNamespce,
      String datastoreKind,
      String topicId,
      String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlp = DlpServiceClient.create()) {
      // Specify the Datastore entity to be inspected.
      PartitionId partitionId =
          PartitionId.newBuilder()
              .setProjectId(projectId)
              .setNamespaceId(datastoreNamespce)
              .build();
      KindExpression kindExpression = KindExpression.newBuilder().setName(datastoreKind).build();

      DatastoreOptions datastoreOptions =
          DatastoreOptions.newBuilder().setKind(kindExpression).setPartitionId(partitionId).build();

      StorageConfig storageConfig =
          StorageConfig.newBuilder().setDatastoreOptions(datastoreOptions).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      List<InfoType> infoTypes =
          Stream.of("PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD_NUMBER")
              .map(it -> InfoType.newBuilder().setName(it).build())
              .collect(Collectors.toList());

      // Specify how the content should be inspected.
      InspectConfig inspectConfig =
          InspectConfig.newBuilder().addAllInfoTypes(infoTypes).setIncludeQuote(true).build();

      // Specify the action that is triggered when the job completes.
      String pubSubTopic = String.format("projects/%s/topics/%s", projectId, topicId);
      Action.PublishToPubSub publishToPubSub =
          Action.PublishToPubSub.newBuilder().setTopic(pubSubTopic).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the long running job we want the service to perform.
      InspectJobConfig inspectJobConfig =
          InspectJobConfig.newBuilder()
              .setStorageConfig(storageConfig)
              .setInspectConfig(inspectConfig)
              .addActions(action)
              .build();

      // Create the request for the job configured above.
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setInspectJob(inspectJobConfig)
              .build();

      // Use the client to send the request.
      final DlpJob dlpJob = dlp.createDlpJob(createDlpJobRequest);
      System.out.println("Job created: " + dlpJob.getName());

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Get the latest state of the job from the service
      GetDlpJobRequest request = GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();
      DlpJob completedJob = dlp.getDlpJob(request);

      // Parse the response and process results.
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());
      InspectDataSourceDetails.Result result = completedJob.getInspectDetails().getResult();
      System.out.println("Findings: ");
      for (InfoTypeStats infoTypeStat : result.getInfoTypeStatsList()) {
        System.out.print("\tInfo type: " + infoTypeStat.getInfoType().getName());
        System.out.println("\tCount: " + infoTypeStat.getCount());
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the target Datastore is stored under
// This may or may not equal the calling project ID
// const dataProjectId = 'my-project';

// (Optional) The ID namespace of the Datastore document to inspect.
// To ignore Datastore namespaces, set this to an empty string ('')
// const namespaceId = '';

// The kind of the Datastore entity to inspect.
// const kind = 'Person';

// The minimum likelihood required before returning a match
// const minLikelihood = 'LIKELIHOOD_UNSPECIFIED';

// The maximum number of findings to report per request (0 = server maximum)
// const maxFindings = 0;

// The infoTypes of information to match
// const infoTypes = [{ name: 'PHONE_NUMBER' }, { name: 'EMAIL_ADDRESS' }, { name: 'CREDIT_CARD_NUMBER' }];

// The customInfoTypes of information to match
// const customInfoTypes = [{ infoType: { name: 'DICT_TYPE' }, dictionary: { wordList: { words: ['foo', 'bar', 'baz']}}},
//   { infoType: { name: 'REGEX_TYPE' }, regex: {pattern: '\\(\\d{3}\\) \\d{3}-\\d{4}'}}];

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

async function inspectDatastore() {
  // Construct items to be inspected
  const storageItems = {
    datastoreOptions: {
      partitionId: {
        projectId: dataProjectId,
        namespaceId: namespaceId,
      },
      kind: {
        name: kind,
      },
    },
  };

  // Construct request for creating an inspect job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectJob: {
      inspectConfig: {
        infoTypes: infoTypes,
        customInfoTypes: customInfoTypes,
        minLikelihood: minLikelihood,
        limits: {
          maxFindingsPerRequest: maxFindings,
        },
      },
      storageConfig: storageItems,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };
  // Run inspect-job creation request
  const [topicResponse] = await pubsub.topic(topicId).get();
  // Verify the Pub/Sub topic and listen for job notifications via an
  // existing subscription.
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  // Wait for DLP job to fully complete
  setTimeout(() => {
    console.log('Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  console.log(`Job ${job.name} status: ${job.state}`);

  const infoTypeStats = job.inspectDetails.result.infoTypeStats;
  if (infoTypeStats.length > 0) {
    infoTypeStats.forEach(infoTypeStat => {
      console.log(
        `  Found ${infoTypeStat.count} instance(s) of infoType ${infoTypeStat.infoType.name}.`
      );
    });
  } else {
    console.log('No findings.');
  }
}
await inspectDatastore();

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import threading
from typing import List, Optional

import google.cloud.dlp
import google.cloud.pubsub


def inspect_datastore(
    project: str,
    datastore_project: str,
    kind: str,
    topic_id: str,
    subscription_id: str,
    info_types: List[str],
    custom_dictionaries: List[str] = None,
    custom_regexes: List[str] = None,
    namespace_id: str = None,
    min_likelihood: Optional[int] = None,
    max_findings: Optional[int] = None,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to analyze Datastore data.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        datastore_project: The Google Cloud project id of the target Datastore.
        kind: The kind of the Datastore entity to inspect, e.g. 'Person'.
        topic_id: The id of the Cloud Pub/Sub topic to which the API will
            broadcast job completion. The topic must already exist.
        subscription_id: The id of the Cloud Pub/Sub subscription to listen on
            while waiting for job completion. The subscription must already
            exist and be subscribed to the topic.
        info_types: A list of strings representing info types to look for.
            A full list of info type categories can be fetched from the API.
        namespace_id: The namespace of the Datastore document, if applicable.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        timeout: The number of seconds to wait for a response from the API.
    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries (protos are also accepted).
    if not info_types:
        info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
    info_types = [{"name": info_type} for info_type in info_types]

    # Prepare custom_info_types by parsing the dictionary word lists and
    # regex patterns.
    if custom_dictionaries is None:
        custom_dictionaries = []
    dictionaries = [
        {
            "info_type": {"name": f"CUSTOM_DICTIONARY_{i}"},
            "dictionary": {"word_list": {"words": custom_dict.split(",")}},
        }
        for i, custom_dict in enumerate(custom_dictionaries)
    ]
    if custom_regexes is None:
        custom_regexes = []
    regexes = [
        {
            "info_type": {"name": f"CUSTOM_REGEX_{i}"},
            "regex": {"pattern": custom_regex},
        }
        for i, custom_regex in enumerate(custom_regexes)
    ]
    custom_info_types = dictionaries + regexes

    # Construct the configuration dictionary. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": info_types,
        "custom_info_types": custom_info_types,
        "min_likelihood": min_likelihood,
        "limits": {"max_findings_per_request": max_findings},
    }

    # Construct a storage_config containing the target Datastore info.
    storage_config = {
        "datastore_options": {
            "partition_id": {
                "project_id": datastore_project,
                "namespace_id": namespace_id,
            },
            "kind": {"name": kind},
        }
    }

    # Convert the project id into full resource ids.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Construct the inspect_job, which defines the entire inspect content task.
    inspect_job = {
        "inspect_config": inspect_config,
        "storage_config": storage_config,
        "actions": actions,
    }

    operation = dlp.create_dlp_job(
        request={"parent": parent, "inspect_job": inspect_job}
    )
    print(f"Inspection operation started: {operation.name}")

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)

    # Set up a callback to acknowledge a message. This closes around an event
    # so that it can signal that it is done and the main thread can continue.
    job_done = threading.Event()

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        try:
            if message.attributes["DlpJobName"] == operation.name:
                # This is the message we're looking for, so acknowledge it.
                message.ack()

                # Now that the job is done, fetch the results and print them.
                job = dlp.get_dlp_job(request={"name": operation.name})
                print(f"Job name: {job.name}")
                if job.inspect_details.result.info_type_stats:
                    for finding in job.inspect_details.result.info_type_stats:
                        print(
                            f"Info type: {finding.info_type.name}; Count: {finding.count}"
                        )
                else:
                    print("No findings.")

                # Signal to the main thread that we can exit.
                job_done.set()
            else:
                # This is not the message we're looking for.
                message.drop()
        except Exception as e:
            # Because this is executing in a thread, an exception won't be
            # noted unless we print it manually.
            print(e)
            raise

    # Register the callback and wait on the event.
    subscriber.subscribe(subscription_path, callback=callback)

    finished = job_done.wait(timeout=timeout)
    if not finished:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// inspectDatastore searches for the given info types in the given dataset kind.
func inspectDatastore(w io.Writer, projectID string, infoTypeNames []string, customDictionaries []string, customRegexes []string, pubSubTopic, pubSubSub, dataProject, namespaceID, kind string) error {
	// projectID := "my-project-id"
	// infoTypeNames := []string{"US_SOCIAL_SECURITY_NUMBER"}
	// customDictionaries := []string{...}
	// customRegexes := []string{...}
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// namespaceID := "namespace-id"
	// kind := "MyKind"

	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Convert the info type strings to a list of InfoTypes.
	var infoTypes []*dlppb.InfoType
	for _, it := range infoTypeNames {
		infoTypes = append(infoTypes, &dlppb.InfoType{Name: it})
	}
	// Convert the custom dictionary word lists and custom regexes to a list of CustomInfoTypes.
	var customInfoTypes []*dlppb.CustomInfoType
	for idx, it := range customDictionaries {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_DICTIONARY_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Dictionary_{
				Dictionary: &dlppb.CustomInfoType_Dictionary{
					Source: &dlppb.CustomInfoType_Dictionary_WordList_{
						WordList: &dlppb.CustomInfoType_Dictionary_WordList{
							Words: strings.Split(it, ","),
						},
					},
				},
			},
		})
	}
	for idx, it := range customRegexes {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_REGEX_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Regex_{
				Regex: &dlppb.CustomInfoType_Regex{
					Pattern: it,
				},
			},
		})
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("pubsub.NewClient: %w", err)
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	if exists, err := t.Exists(ctx); err != nil {
		return fmt.Errorf("t.Exists: %w", err)
	} else if !exists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return fmt.Errorf("CreateTopic: %w", err)
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	if exists, err := s.Exists(ctx); err != nil {
		return fmt.Errorf("s.Exists: %w", err)
	} else if !exists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return fmt.Errorf("CreateSubscription: %w", err)
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_InspectJob{
			InspectJob: &dlppb.InspectJobConfig{
				// StorageConfig describes where to find the data.
				StorageConfig: &dlppb.StorageConfig{
					Type: &dlppb.StorageConfig_DatastoreOptions{
						DatastoreOptions: &dlppb.DatastoreOptions{
							PartitionId: &dlppb.PartitionId{
								ProjectId:   dataProject,
								NamespaceId: namespaceID,
							},
							Kind: &dlppb.KindExpression{
								Name: kind,
							},
						},
					},
				},
				// InspectConfig describes what fields to look for.
				InspectConfig: &dlppb.InspectConfig{
					InfoTypes:       infoTypes,
					CustomInfoTypes: customInfoTypes,
					MinLikelihood:   dlppb.Likelihood_POSSIBLE,
					Limits: &dlppb.InspectConfig_FindingLimits{
						MaxFindingsPerRequest: 10,
					},
					IncludeQuote: true,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the inspect job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the inspect job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()

		// Stop listening for more messages.
		defer cancel()

		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "Error getting completed job: %v\n", err)
			return
		}
		r := resp.GetInspectDetails().GetResult().GetInfoTypeStats()
		if len(r) == 0 {
			fmt.Fprintf(w, "No results")
			return
		}
		for _, s := range r {
			fmt.Fprintf(w, "  Found %v instances of infoType %v\n", s.GetCount(), s.GetInfoType().GetName())
		}
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DatastoreOptions;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectConfig\FindingLimits;
use Google\Cloud\Dlp\V2\InspectJobConfig;
use Google\Cloud\Dlp\V2\KindExpression;
use Google\Cloud\Dlp\V2\Likelihood;
use Google\Cloud\Dlp\V2\PartitionId;
use Google\Cloud\Dlp\V2\StorageConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Inspect Datastore, using Pub/Sub for job status notifications.
 *
 * @param string $callingProjectId  The project ID to run the API call under
 * @param string $dataProjectId     The project ID containing the target Datastore
 * @param string $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string $kind              The datastore kind to inspect
 * @param string $namespaceId       The ID namespace of the Datastore document to inspect
 * @param int    $maxFindings       (Optional) The maximum number of findings to report per request (0 = server maximum)
 */
function inspect_datastore(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $kind,
    string $namespaceId,
    int $maxFindings = 0
): void {
    // Instantiate clients
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // The infoTypes of information to match
    $personNameInfoType = (new InfoType())
        ->setName('PERSON_NAME');
    $phoneNumberInfoType = (new InfoType())
        ->setName('PHONE_NUMBER');
    $infoTypes = [$personNameInfoType, $phoneNumberInfoType];

    // The minimum likelihood required before returning a match
    $minLikelihood = likelihood::LIKELIHOOD_UNSPECIFIED;

    // Specify finding limits
    $limits = (new FindingLimits())
        ->setMaxFindingsPerRequest($maxFindings);

    // Construct items to be inspected
    $partitionId = (new PartitionId())
        ->setProjectId($dataProjectId)
        ->setNamespaceId($namespaceId);

    $kindExpression = (new KindExpression())
        ->setName($kind);

    $datastoreOptions = (new DatastoreOptions())
        ->setPartitionId($partitionId)
        ->setKind($kindExpression);

    // Construct the inspect config object
    $inspectConfig = (new InspectConfig())
        ->setInfoTypes($infoTypes)
        ->setMinLikelihood($minLikelihood)
        ->setLimits($limits);

    // Construct the storage config object
    $storageConfig = (new StorageConfig())
        ->setDatastoreOptions($datastoreOptions);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct inspect job config to run
    $inspectJob = (new InspectJobConfig())
        ->setInspectConfig($inspectConfig)
        ->setStorageConfig($storageConfig)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setInspectJob($inspectJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $infoTypeStats = $job->getInspectDetails()->getResult()->getInfoTypeStats();
            if (count($infoTypeStats) === 0) {
                print('No findings.' . PHP_EOL);
            } else {
                foreach ($infoTypeStats as $infoTypeStat) {
                    printf('  Found %s instance(s) of infoType %s' . PHP_EOL, $infoTypeStat->getCount(), $infoTypeStat->getInfoType()->getName());
                }
            }
            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state.');
    }
}

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

using Google.Api.Gax.ResourceNames;
using Google.Cloud.BigQuery.V2;
using Google.Cloud.Dlp.V2;
using Google.Protobuf.WellKnownTypes;
using System;
using System.Collections.Generic;
using System.Threading;
using static Google.Cloud.Dlp.V2.InspectConfig.Types;

public class InspectCloudDataStore
{
    public static object Inspect(
        string projectId,
        Likelihood minLikelihood,
        int maxFindings,
        bool includeQuote,
        string kindName,
        string namespaceId,
        IEnumerable<InfoType> infoTypes,
        IEnumerable<CustomInfoType> customInfoTypes,
        string datasetId,
        string tableId)
    {
        var inspectJob = new InspectJobConfig
        {
            StorageConfig = new StorageConfig
            {
                DatastoreOptions = new DatastoreOptions
                {
                    Kind = new KindExpression { Name = kindName },
                    PartitionId = new PartitionId
                    {
                        NamespaceId = namespaceId,
                        ProjectId = projectId,
                    }
                },
                TimespanConfig = new StorageConfig.Types.TimespanConfig
                {
                    StartTime = Timestamp.FromDateTime(System.DateTime.UtcNow.AddYears(-1)),
                    EndTime = Timestamp.FromDateTime(System.DateTime.UtcNow)
                }
            },

            InspectConfig = new InspectConfig
            {
                InfoTypes = { infoTypes },
                CustomInfoTypes = { customInfoTypes },
                Limits = new FindingLimits
                {
                    MaxFindingsPerRequest = maxFindings
                },
                ExcludeInfoTypes = false,
                IncludeQuote = includeQuote,
                MinLikelihood = minLikelihood
            },
            Actions =
                {
                    new Google.Cloud.Dlp.V2.Action
                    {
                        // Save results in BigQuery Table
                        SaveFindings = new Google.Cloud.Dlp.V2.Action.Types.SaveFindings
                        {
                            OutputConfig = new OutputStorageConfig
                            {
                                Table = new Google.Cloud.Dlp.V2.BigQueryTable
                                {
                                    ProjectId = projectId,
                                    DatasetId = datasetId,
                                    TableId = tableId
                                }
                            }
                        },
                    }
                }
        };

        // Issue Create Dlp Job Request
        var client = DlpServiceClient.Create();
        var request = new CreateDlpJobRequest
        {
            InspectJob = inspectJob,
            Parent = new LocationName(projectId, "global").ToString(),
        };

        // We need created job name
        var dlpJob = client.CreateDlpJob(request);
        var jobName = dlpJob.Name;

        // Make sure the job finishes before inspecting the results.
        // Alternatively, we can inspect results opportunistically, but
        // for testing purposes, we want consistent outcome
        var finishedJob = EnsureJobFinishes(projectId, jobName);
        var bigQueryClient = BigQueryClient.Create(projectId);
        var table = bigQueryClient.GetTable(datasetId, tableId);

        // Return only first page of 10 rows
        Console.WriteLine("DLP v2 Results:");
        var firstPage = table.ListRows(new ListRowsOptions { StartIndex = 0, PageSize = 10 });
        foreach (var item in firstPage)
        {
            Console.WriteLine($"\t {item[""]}");
        }

        return finishedJob;
    }

    private static DlpJob EnsureJobFinishes(string projectId, string jobName)
    {
        var client = DlpServiceClient.Create();
        var request = new GetDlpJobRequest
        {
            DlpJobName = new DlpJobName(projectId, jobName),
        };

        // Simple logic that gives the job 5*30 sec at most to complete - for testing purposes only
        var numOfAttempts = 5;
        do
        {
            var dlpJob = client.GetDlpJob(request);
            numOfAttempts--;
            if (dlpJob.State != DlpJob.Types.JobState.Running)
            {
                return dlpJob;
            }

            Thread.Sleep(TimeSpan.FromSeconds(30));
        } while (numOfAttempts > 0);

        throw new InvalidOperationException("Job did not complete in time");
    }
}

檢查 BigQuery 資料表

您可以透過 REST 要求，使用 Sensitive Data Protection 設定 BigQuery 資料表檢查作業，也可以使用用戶端程式庫，以多種語言透過程式輔助方式進行設定。

注意： 如要瞭解下列 JSON 和程式碼範例中包含的參數，請參閱本主題稍後的「設定儲存空間檢查」。

重要事項： 如要使用本頁面的程式碼，請先設定 Sensitive Data Protection 用戶端。如要進一步瞭解如何安裝及建立 Sensitive Data Protection 用戶端，請參閱「Sensitive Data Protection 用戶端程式庫」。(將 JSON 傳送至 Sensitive Data Protection REST 端點時，不需要用戶端程式庫)。

如要使用 Sensitive Data Protection 設定 BigQuery 資料表的掃描作業，請按照下列步驟操作：

控制台

如要使用 Sensitive Data Protection 設定 BigQuery 資料表的掃描作業，請按照下列步驟操作：

1. 在 Google Cloud 控制台的「Sensitive Data Protection」專區中，前往「Create job or job trigger」頁面。

   前往「Create job or job trigger」(建立工作或工作觸發條件)

2. 輸入 Sensitive Data Protection 工作資訊，然後點選「Continue」(繼續)，完成各個步驟：

   • 在「Choose input data」(選擇輸入資料) 部分，在「Name」(名稱) 欄位中輸入值，為工作命名。在「Location」(位置) 中，從「Storage type」(儲存空間類型) 選單中選擇「BigQuery」，然後輸入要掃描的資料表資訊。

     「取樣」部分已預先設定，可對資料執行樣本掃描。如果您有大量資料，可以調整「Limit rows by」(資料列限制依據) 和「Maximum number of rows」(資料列數量上限) 欄位，節省資源。詳情請參閱「選擇輸入資料」。

   • (選用) 如果您希望將每個發現項目連結至包含該項目的資料列，請設定「Identifying fields」(識別欄位) 欄位。

     輸入資料欄名稱，用於唯一識別資料表中的每個資料列。如有需要，請使用點標記法指定巢狀結構欄位。您可以視需要新增多個欄位。

     您也必須開啟「儲存至 BigQuery」動作，才能將發現項目匯出至 BigQuery。發現項目匯出至 BigQuery 時，每個發現項目都會包含識別欄位的對應值。詳情請參閱 identifyingFields 的說明。

   • (選用) 在「設定偵測作業」部分，您可以設定要尋找的資料類型，也就是「infoTypes」。您可以從預先定義的 infoType 清單中選取，也可以選取現有範本。詳情請參閱「設定偵測功能」。

   • 在「Add actions」(新增動作) 部分，選取一或多項動作，讓 Sensitive Data Protection 在工作完成後執行。詳情請參閱「啟用檢查或風險分析動作」。

     選取動作後，按一下「繼續」。

   • (選用) 如要排程，但只想執行一次掃描，請將選單設為「無」。如要排定定期執行掃描作業，請按一下「Create a trigger to run the job on a periodic schedule」(建立觸發條件來定期執行工作)。詳情請參閱「排程」。

3. 點選「建立」。

4. Sensitive Data Protection 工作完成後，系統會將您重新導向至工作詳細資料頁面，並透過電子郵件通知您。您可以在工作詳細資料頁面查看檢查結果。

5. (選用) 如果您選擇將 Sensitive Data Protection 發現結果發布至 BigQuery，請在「工作詳細資料」頁面中，按一下「在 BigQuery 中查看發現結果」，在 BigQuery 網頁版 UI 中開啟資料表。接著，您可以查詢資料表並分析結果。如要進一步瞭解如何在 BigQuery 中查詢結果，請參閱「查詢 Sensitive Data Protection 在 BigQuery 的發現結果」。

通訊協定

以下是 JSON 範例，可透過 POST 要求傳送至指定的 DLP API REST 端點。這個 JSON 範例說明如何使用 DLP API 檢查 BigQuery 資料表。如要瞭解要求中包含的參數，請參閱本主題後續的「設定儲存空間檢查」一節。

您可以在 dlpJobs.create 的參考頁面上，透過 APIs Explorer 快速試用這項功能：

前往 API Explorer

請注意，即使是在 APIs Explorer 中，成功的要求也會建立新的掃描工作。如要瞭解如何控管掃描工作，請參閱本主題稍後的「擷取檢查結果」。如需使用 JSON 向 DLP API 傳送要求的一般資訊，請參閱 JSON 快速入門導覽課程。

JSON 輸入：

POST https://dlp.googleapis.com/v2/projects/[PROJECT-ID]/dlpJobs?key={YOUR_API_KEY}

{
  "inspectJob":{
    "storageConfig":{
      "bigQueryOptions":{
        "tableReference":{
          "projectId":"[PROJECT-ID]",
          "datasetId":"[BIGQUERY-DATASET-NAME]",
          "tableId":"[BIGQUERY-TABLE-NAME]"
        },
        "identifyingFields":[
          {
            "name":"id"
          }
        ]
      },
      "timespanConfig":{
        "startTime":"2017-11-13T12:34:29.965633345Z ",
        "endTime":"2018-01-05T04:45:04.240912125Z "
      }
    },
    "inspectConfig":{
      "infoTypes":[
        {
          "name":"PHONE_NUMBER"
        }
      ],
      "excludeInfoTypes":false,
      "includeQuote":true,
      "minLikelihood":"LIKELY"
    },
    "actions":[
      {
        "saveFindings":{
          "outputConfig":{
            "table":{
              "projectId":"[PROJECT-ID]",
              "datasetId":"[BIGQUERY-DATASET-NAME]",
              "tableId":"[BIGQUERY-TABLE-NAME]"
            },
            "outputSchema": "BASIC_COLUMNS"
          }
        }
      }
    ]
  }
}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.BigQueryOptions;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InfoTypeStats;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectDataSourceDetails;
import com.google.privacy.dlp.v2.InspectJobConfig;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.StorageConfig;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.stream.Collectors;
import java.util.stream.Stream;

public class InspectBigQueryTable {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String bigQueryDatasetId = "your-bigquery-dataset-id";
    String bigQueryTableId = "your-bigquery-table-id";
    String topicId = "your-pubsub-topic-id";
    String subscriptionId = "your-pubsub-subscription-id";
    inspectBigQueryTable(projectId, bigQueryDatasetId, bigQueryTableId, topicId, subscriptionId);
  }

  // Inspects a BigQuery Table
  public static void inspectBigQueryTable(
      String projectId,
      String bigQueryDatasetId,
      String bigQueryTableId,
      String topicId,
      String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlp = DlpServiceClient.create()) {
      // Specify the BigQuery table to be inspected.
      BigQueryTable tableReference =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(bigQueryDatasetId)
              .setTableId(bigQueryTableId)
              .build();

      BigQueryOptions bigQueryOptions =
          BigQueryOptions.newBuilder().setTableReference(tableReference).build();

      StorageConfig storageConfig =
          StorageConfig.newBuilder().setBigQueryOptions(bigQueryOptions).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      List<InfoType> infoTypes =
          Stream.of("PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD_NUMBER")
              .map(it -> InfoType.newBuilder().setName(it).build())
              .collect(Collectors.toList());

      // Specify how the content should be inspected.
      InspectConfig inspectConfig =
          InspectConfig.newBuilder().addAllInfoTypes(infoTypes).setIncludeQuote(true).build();

      // Specify the action that is triggered when the job completes.
      String pubSubTopic = String.format("projects/%s/topics/%s", projectId, topicId);
      Action.PublishToPubSub publishToPubSub =
          Action.PublishToPubSub.newBuilder().setTopic(pubSubTopic).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the long running job we want the service to perform.
      InspectJobConfig inspectJobConfig =
          InspectJobConfig.newBuilder()
              .setStorageConfig(storageConfig)
              .setInspectConfig(inspectConfig)
              .addActions(action)
              .build();

      // Create the request for the job configured above.
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setInspectJob(inspectJobConfig)
              .build();

      // Use the client to send the request.
      final DlpJob dlpJob = dlp.createDlpJob(createDlpJobRequest);
      System.out.println("Job created: " + dlpJob.getName());

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Get the latest state of the job from the service
      GetDlpJobRequest request = GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();
      DlpJob completedJob = dlp.getDlpJob(request);

      // Parse the response and process results.
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());
      InspectDataSourceDetails.Result result = completedJob.getInspectDetails().getResult();
      System.out.println("Findings: ");
      for (InfoTypeStats infoTypeStat : result.getInfoTypeStatsList()) {
        System.out.print("\tInfo type: " + infoTypeStat.getInfoType().getName());
        System.out.println("\tCount: " + infoTypeStat.getCount());
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const dataProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The minimum likelihood required before returning a match
// const minLikelihood = 'LIKELIHOOD_UNSPECIFIED';

// The maximum number of findings to report per request (0 = server maximum)
// const maxFindings = 0;

// The infoTypes of information to match
// const infoTypes = [{ name: 'PHONE_NUMBER' }, { name: 'EMAIL_ADDRESS' }, { name: 'CREDIT_CARD_NUMBER' }];

// The customInfoTypes of information to match
// const customInfoTypes = [{ infoType: { name: 'DICT_TYPE' }, dictionary: { wordList: { words: ['foo', 'bar', 'baz']}}},
//   { infoType: { name: 'REGEX_TYPE' }, regex: {pattern: '\\(\\d{3}\\) \\d{3}-\\d{4}'}}];

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

async function inspectBigquery() {
  // Construct item to be inspected
  const storageItem = {
    bigQueryOptions: {
      tableReference: {
        projectId: dataProjectId,
        datasetId: datasetId,
        tableId: tableId,
      },
    },
  };

  // Construct request for creating an inspect job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectJob: {
      inspectConfig: {
        infoTypes: infoTypes,
        customInfoTypes: customInfoTypes,
        minLikelihood: minLikelihood,
        limits: {
          maxFindingsPerRequest: maxFindings,
        },
      },
      storageConfig: storageItem,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Run inspect-job creation request
  const [topicResponse] = await pubsub.topic(topicId).get();
  // Verify the Pub/Sub topic and listen for job notifications via an
  // existing subscription.
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  // Wait for DLP job to fully complete
  setTimeout(() => {
    console.log('Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  console.log(`Job ${job.name} status: ${job.state}`);

  const infoTypeStats = job.inspectDetails.result.infoTypeStats;
  if (infoTypeStats.length > 0) {
    infoTypeStats.forEach(infoTypeStat => {
      console.log(
        `  Found ${infoTypeStat.count} instance(s) of infoType ${infoTypeStat.infoType.name}.`
      );
    });
  } else {
    console.log('No findings.');
  }
}

await inspectBigquery();

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import threading
from typing import List, Optional

import google.cloud.dlp
import google.cloud.pubsub


def inspect_bigquery(
    project: str,
    bigquery_project: str,
    dataset_id: str,
    table_id: str,
    topic_id: str,
    subscription_id: str,
    info_types: List[str],
    custom_dictionaries: List[str] = None,
    custom_regexes: List[str] = None,
    min_likelihood: Optional[int] = None,
    max_findings: Optional[int] = None,
    timeout: int = 500,
) -> None:
    """Uses the Data Loss Prevention API to analyze BigQuery data.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        bigquery_project: The Google Cloud project id of the target table.
        dataset_id: The id of the target BigQuery dataset.
        table_id: The id of the target BigQuery table.
        topic_id: The id of the Cloud Pub/Sub topic to which the API will
            broadcast job completion. The topic must already exist.
        subscription_id: The id of the Cloud Pub/Sub subscription to listen on
            while waiting for job completion. The subscription must already
            exist and be subscribed to the topic.
        info_types: A list of strings representing info types to look for.
            A full list of info type categories can be fetched from the API.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        timeout: The number of seconds to wait for a response from the API.
    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries (protos are also accepted).
    if not info_types:
        info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
    info_types = [{"name": info_type} for info_type in info_types]

    # Prepare custom_info_types by parsing the dictionary word lists and
    # regex patterns.
    if custom_dictionaries is None:
        custom_dictionaries = []
    dictionaries = [
        {
            "info_type": {"name": f"CUSTOM_DICTIONARY_{i}"},
            "dictionary": {"word_list": {"words": custom_dict.split(",")}},
        }
        for i, custom_dict in enumerate(custom_dictionaries)
    ]
    if custom_regexes is None:
        custom_regexes = []
    regexes = [
        {
            "info_type": {"name": f"CUSTOM_REGEX_{i}"},
            "regex": {"pattern": custom_regex},
        }
        for i, custom_regex in enumerate(custom_regexes)
    ]
    custom_info_types = dictionaries + regexes

    # Construct the configuration dictionary. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": info_types,
        "custom_info_types": custom_info_types,
        "min_likelihood": min_likelihood,
        "limits": {"max_findings_per_request": max_findings},
    }

    # Construct a storage_config containing the target Bigquery info.
    storage_config = {
        "big_query_options": {
            "table_reference": {
                "project_id": bigquery_project,
                "dataset_id": dataset_id,
                "table_id": table_id,
            }
        }
    }

    # Convert the project id into full resource ids.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Construct the inspect_job, which defines the entire inspect content task.
    inspect_job = {
        "inspect_config": inspect_config,
        "storage_config": storage_config,
        "actions": actions,
    }

    operation = dlp.create_dlp_job(
        request={"parent": parent, "inspect_job": inspect_job}
    )
    print(f"Inspection operation started: {operation.name}")

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)

    # Set up a callback to acknowledge a message. This closes around an event
    # so that it can signal that it is done and the main thread can continue.
    job_done = threading.Event()

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        try:
            if message.attributes["DlpJobName"] == operation.name:
                # This is the message we're looking for, so acknowledge it.
                message.ack()

                # Now that the job is done, fetch the results and print them.
                job = dlp.get_dlp_job(request={"name": operation.name})
                print(f"Job name: {job.name}")
                if job.inspect_details.result.info_type_stats:
                    for finding in job.inspect_details.result.info_type_stats:
                        print(
                            "Info type: {}; Count: {}".format(
                                finding.info_type.name, finding.count
                            )
                        )
                else:
                    print("No findings.")

                # Signal to the main thread that we can exit.
                job_done.set()
            else:
                # This is not the message we're looking for.
                message.drop()
        except Exception as e:
            # Because this is executing in a thread, an exception won't be
            # noted unless we print it manually.
            print(e)
            raise

    # Register the callback and wait on the event.
    subscriber.subscribe(subscription_path, callback=callback)
    finished = job_done.wait(timeout=timeout)
    if not finished:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// inspectBigquery searches for the given info types in the given Bigquery dataset table.
func inspectBigquery(w io.Writer, projectID string, infoTypeNames []string, customDictionaries []string, customRegexes []string, pubSubTopic, pubSubSub, dataProject, datasetID, tableID string) error {
	// projectID := "my-project-id"
	// infoTypeNames := []string{"US_SOCIAL_SECURITY_NUMBER"}
	// customDictionaries := []string{...}
	// customRegexes := []string{...}
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// dataProject := "my-data-project-ID"
	// datasetID := "my_dataset"
	// tableID := "mytable"

	ctx := context.Background()

	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Convert the info type strings to a list of InfoTypes.
	var infoTypes []*dlppb.InfoType
	for _, it := range infoTypeNames {
		infoTypes = append(infoTypes, &dlppb.InfoType{Name: it})
	}
	// Convert the custom dictionary word lists and custom regexes to a list of CustomInfoTypes.
	var customInfoTypes []*dlppb.CustomInfoType
	for idx, it := range customDictionaries {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_DICTIONARY_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Dictionary_{
				Dictionary: &dlppb.CustomInfoType_Dictionary{
					Source: &dlppb.CustomInfoType_Dictionary_WordList_{
						WordList: &dlppb.CustomInfoType_Dictionary_WordList{
							Words: strings.Split(it, ","),
						},
					},
				},
			},
		})
	}
	for idx, it := range customRegexes {
		customInfoTypes = append(customInfoTypes, &dlppb.CustomInfoType{
			InfoType: &dlppb.InfoType{
				Name: fmt.Sprintf("CUSTOM_REGEX_%d", idx),
			},
			Type: &dlppb.CustomInfoType_Regex_{
				Regex: &dlppb.CustomInfoType_Regex{
					Pattern: it,
				},
			},
		})
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("pubsub.NewClient: %w", err)
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	if exists, err := t.Exists(ctx); err != nil {
		return fmt.Errorf("t.Exists: %w", err)
	} else if !exists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return fmt.Errorf("CreateTopic: %w", err)
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	if exists, err := s.Exists(ctx); err != nil {
		return fmt.Errorf("s.Exits: %w", err)
	} else if !exists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return fmt.Errorf("CreateSubscription: %w", err)
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_InspectJob{
			InspectJob: &dlppb.InspectJobConfig{
				// StorageConfig describes where to find the data.
				StorageConfig: &dlppb.StorageConfig{
					Type: &dlppb.StorageConfig_BigQueryOptions{
						BigQueryOptions: &dlppb.BigQueryOptions{
							TableReference: &dlppb.BigQueryTable{
								ProjectId: dataProject,
								DatasetId: datasetID,
								TableId:   tableID,
							},
						},
					},
				},
				// InspectConfig describes what fields to look for.
				InspectConfig: &dlppb.InspectConfig{
					InfoTypes:       infoTypes,
					CustomInfoTypes: customInfoTypes,
					MinLikelihood:   dlppb.Likelihood_POSSIBLE,
					Limits: &dlppb.InspectConfig_FindingLimits{
						MaxFindingsPerRequest: 10,
					},
					IncludeQuote: true,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the inspect job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the inspect job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()

		// Stop listening for more messages.
		defer cancel()

		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "Error getting completed job: %v\n", err)
			return
		}
		r := resp.GetInspectDetails().GetResult().GetInfoTypeStats()
		if len(r) == 0 {
			fmt.Fprintf(w, "No results")
			return
		}
		for _, s := range r {
			fmt.Fprintf(w, "  Found %v instances of infoType %v\n", s.GetCount(), s.GetInfoType().GetName())
		}
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryOptions;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectConfig\FindingLimits;
use Google\Cloud\Dlp\V2\InspectJobConfig;
use Google\Cloud\Dlp\V2\Likelihood;
use Google\Cloud\Dlp\V2\StorageConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Inspect a BigQuery table , using Pub/Sub for job status notifications.
 *
 * @param string $callingProjectId  The project ID to run the API call under
 * @param string $dataProjectId     The project ID containing the target Datastore
 * @param string $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string $datasetId         The ID of the dataset to inspect
 * @param string $tableId           The ID of the table to inspect
 * @param int    $maxFindings       (Optional) The maximum number of findings to report per request (0 = server maximum)
 */
function inspect_bigquery(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $datasetId,
    string $tableId,
    int $maxFindings = 0
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // The infoTypes of information to match
    $personNameInfoType = (new InfoType())
        ->setName('PERSON_NAME');
    $creditCardNumberInfoType = (new InfoType())
        ->setName('CREDIT_CARD_NUMBER');
    $infoTypes = [$personNameInfoType, $creditCardNumberInfoType];

    // The minimum likelihood required before returning a match
    $minLikelihood = likelihood::LIKELIHOOD_UNSPECIFIED;

    // Specify finding limits
    $limits = (new FindingLimits())
        ->setMaxFindingsPerRequest($maxFindings);

    // Construct items to be inspected
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($dataProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    $bigQueryOptions = (new BigQueryOptions())
        ->setTableReference($bigqueryTable);

    $storageConfig = (new StorageConfig())
        ->setBigQueryOptions($bigQueryOptions);

    // Construct the inspect config object
    $inspectConfig = (new InspectConfig())
        ->setMinLikelihood($minLikelihood)
        ->setLimits($limits)
        ->setInfoTypes($infoTypes);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct inspect job config to run
    $inspectJob = (new InspectJobConfig())
        ->setInspectConfig($inspectConfig)
        ->setStorageConfig($storageConfig)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setInspectJob($inspectJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $infoTypeStats = $job->getInspectDetails()->getResult()->getInfoTypeStats();
            if (count($infoTypeStats) === 0) {
                print('No findings.' . PHP_EOL);
            } else {
                foreach ($infoTypeStats as $infoTypeStat) {
                    printf(
                        '  Found %s instance(s) of infoType %s' . PHP_EOL,
                        $infoTypeStat->getCount(),
                        $infoTypeStat->getInfoType()->getName()
                    );
                }
            }
            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

using Google.Api.Gax.ResourceNames;
using Google.Cloud.BigQuery.V2;
using Google.Cloud.Dlp.V2;
using Google.Protobuf.WellKnownTypes;
using System;
using System.Collections.Generic;
using System.Threading;
using static Google.Cloud.Dlp.V2.InspectConfig.Types;

public class InspectBigQuery
{
    public static object Inspect(
        string projectId,
        Likelihood minLikelihood,
        int maxFindings,
        bool includeQuote,
        IEnumerable<FieldId> identifyingFields,
        IEnumerable<InfoType> infoTypes,
        IEnumerable<CustomInfoType> customInfoTypes,
        string datasetId,
        string tableId)
    {
        var inspectJob = new InspectJobConfig
        {
            StorageConfig = new StorageConfig
            {
                BigQueryOptions = new BigQueryOptions
                {
                    TableReference = new Google.Cloud.Dlp.V2.BigQueryTable
                    {
                        ProjectId = projectId,
                        DatasetId = datasetId,
                        TableId = tableId,
                    },
                    IdentifyingFields =
                        {
                            identifyingFields
                        }
                },

                TimespanConfig = new StorageConfig.Types.TimespanConfig
                {
                    StartTime = Timestamp.FromDateTime(System.DateTime.UtcNow.AddYears(-1)),
                    EndTime = Timestamp.FromDateTime(System.DateTime.UtcNow)
                }
            },

            InspectConfig = new InspectConfig
            {
                InfoTypes = { infoTypes },
                CustomInfoTypes = { customInfoTypes },
                Limits = new FindingLimits
                {
                    MaxFindingsPerRequest = maxFindings
                },
                ExcludeInfoTypes = false,
                IncludeQuote = includeQuote,
                MinLikelihood = minLikelihood
            },
            Actions =
                {
                    new Google.Cloud.Dlp.V2.Action
                    {
                        // Save results in BigQuery Table
                        SaveFindings = new Google.Cloud.Dlp.V2.Action.Types.SaveFindings
                        {
                            OutputConfig = new OutputStorageConfig
                            {
                                Table = new Google.Cloud.Dlp.V2.BigQueryTable
                                {
                                    ProjectId = projectId,
                                    DatasetId = datasetId,
                                    TableId = tableId
                                }
                            }
                        },
                    }
                }
        };

        // Issue Create Dlp Job Request
        var client = DlpServiceClient.Create();
        var request = new CreateDlpJobRequest
        {
            InspectJob = inspectJob,
            Parent = new LocationName(projectId, "global").ToString(),
        };

        // We need created job name
        var dlpJob = client.CreateDlpJob(request);
        var jobName = dlpJob.Name;

        // Make sure the job finishes before inspecting the results.
        // Alternatively, we can inspect results opportunistically, but
        // for testing purposes, we want consistent outcome
        var finishedJob = EnsureJobFinishes(projectId, jobName);
        var bigQueryClient = BigQueryClient.Create(projectId);
        var table = bigQueryClient.GetTable(datasetId, tableId);

        // Return only first page of 10 rows
        Console.WriteLine("DLP v2 Results:");
        var firstPage = table.ListRows(new ListRowsOptions { StartIndex = 0, PageSize = 10 });
        foreach (var item in firstPage)
        {
            Console.WriteLine($"\t {item[""]}");
        }

        return finishedJob;
    }

    private static DlpJob EnsureJobFinishes(string projectId, string jobName)
    {
        var client = DlpServiceClient.Create();
        var request = new GetDlpJobRequest
        {
            DlpJobName = new DlpJobName(projectId, jobName),
        };

        // Simple logic that gives the job 5*30 sec at most to complete - for testing purposes only
        var numOfAttempts = 5;
        do
        {
            var dlpJob = client.GetDlpJob(request);
            numOfAttempts--;
            if (dlpJob.State != DlpJob.Types.JobState.Running)
            {
                return dlpJob;
            }

            Thread.Sleep(TimeSpan.FromSeconds(30));
        } while (numOfAttempts > 0);

        throw new InvalidOperationException("Job did not complete in time");
    }
}

設定儲存空間檢查

如要檢查 Cloud Storage 位置、Datastore 種類或 BigQuery 資料表，請將要求傳送至 DLP API 的 projects.dlpJobs.create 方法，其中至少包含要掃描的資料位置和掃描內容。除了這些必要參數以外，您也可以指定寫入掃描結果的位置、大小和可能性門檻等項目。要求成功後，系統會建立 DlpJob 物件執行個體，詳情請參閱「擷取檢查結果」。

以下摘要說明可用的設定選項：

• InspectJobConfig 物件：包含檢查工作的設定資訊。請注意，InspectJobConfig 物件也會由 JobTriggers 物件用於排定 DlpJob 的建立作業。這個物件包含：

  • StorageConfig 物件：必要。包含要掃描的儲存空間存放區的相關詳細資料：

    • StorageConfig 物件必須包含下列其中一項，具體取決於要掃描的儲存空間存放區類型：

    • CloudStorageOptions 物件：包含要掃描的 Cloud Storage bucket 相關資訊。

    • DatastoreOptions 物件：包含要掃描的 Datastore 資料集相關資訊。

    • BigQueryOptions object：包含要掃描的 BigQuery 資料表相關資訊 (以及選用的識別欄位)。這個物件也能啟用結果取樣。詳情請參閱下方的「啟用結果取樣」。

    • TimespanConfig object：選用。指定要納入掃描的項目時間範圍。

  • InspectConfig 物件：必要。指定要掃描的內容，例如 infoTypes 和可能性值。

    • InfoType 物件： 必要欄位。要掃描的一或多個 infoType 值。
    • Likelihood enumeration：選用。設定後，Sensitive Data Protection 只會傳回等於或高於這個可能性門檻的發現項目。如果省略這個列舉，預設值為 POSSIBLE。
    • FindingLimits object：選用。設定後，這個物件可讓您指定傳回的調查結果數量上限。
    • includeQuote 參數：選用。預設為 false。如果設為 true，每個發現項目都會包含觸發該項目的資料情境引用內容。
    • excludeInfoTypes 參數：選用。預設值為 false。如果設為 true，掃描結果就不會包含發現項目的類型資訊。
    • CustomInfoType 物件：一或多個使用者建立的自訂 infoType。如要進一步瞭解如何建立自訂 infoType，請參閱「建立自訂 infoType 偵測工具」。

  • inspectTemplateName 字串：選用。指定用於在 InspectConfig 物件中填入預設值的範本。如果您已指定 InspectConfig，範本值會合併到其中。

  • Action objects：選用。完成工作時要執行的一或多個動作。系統會按照列出的順序執行各項動作。您可以在這裡指定結果的寫入位置，或是否要將通知發布至 Pub/Sub 主題。

• jobId：選用。Sensitive Data Protection 傳回的工作 ID。如果省略 jobId 或留空，系統會為工作建立 ID。如果指定，工作會指派這個 ID 值。工作 ID 必須是專屬 ID，且只能包含大小寫英文字母、數字和連字號，也就是必須符合下列規則運算式：[a-zA-Z\\d-]+。

限制檢查的內容量

如要掃描 BigQuery 資料表或 Cloud Storage bucket，Sensitive Data Protection 提供掃描資料集子集的方法。這可提供掃描結果樣本，而不會產生掃描整個資料集的潛在費用。

下列各節說明如何限制 Cloud Storage 掃描和 BigQuery 掃描的大小。

限制 Cloud Storage 掃描

您可以限制掃描的資料量，以啟用 Cloud Storage 中的取樣功能。您可以指示 DLP API 只掃描特定大小以下的檔案、特定檔案類型，以及輸入檔案集中特定百分比的檔案總數。如要這麼做，請在 CloudStorageOptions 中指定下列選填欄位：

• bytesLimitPerFile：設定要從檔案掃描的位元組數量上限。如果掃描的檔案大小超過這個值，系統會省略其餘位元組。設定這個欄位不會影響特定檔案類型。詳情請參閱「每個檔案的掃描位元組數上限」。
• fileTypes[]：列出要納入掃描作業的 FileTypes。這項屬性可以設為下列一或多個列舉類型。
• filesLimitPercent：將掃描的檔案數量限制為輸入FileSet的指定百分比。 在此指定 0 或 100，表示沒有限制。
• sampleMethod：如果未掃描所有位元組，如何取樣位元組。只有在與 bytesLimitPerFile 搭配使用時，指定這個值才有意義。如未指定，掃描會從頂端開始。這個欄位可設為下列其中一個值：
  • TOP：掃描會從頂端開始。
  • RANDOM_START：針對每個大於 bytesLimitPerFile 中指定大小的檔案，隨機挑選開始掃描的位移。掃描的位元組會是連續的。

注意： 檢查機密資料時，必須掃描整張圖片，因此圖片檔案類型不支援取樣。系統會根據圖片檔大小，向您收取掃描費用。

下列範例示範如何使用 DLP API，掃描 Cloud Storage bucket 中 90% 的子集，找出人名。掃描會從資料集的隨機位置開始，而且只會包含 200 位元組以下的文字檔案。

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;

public class InspectStorageWithSampling
{
    public static async Task<DlpJob> InspectAsync(
        string projectId,
        string gcsUri,
        string topicId,
        string subId,
        Likelihood minLikelihood = Likelihood.Possible,
        IEnumerable<InfoType> infoTypes = null)
    {

        // Instantiate the dlp client.
        var dlp = DlpServiceClient.Create();

        // Construct Storage config by specifying the GCS file to be inspected
        // and sample method.
        var storageConfig = new StorageConfig
        {
            CloudStorageOptions = new CloudStorageOptions
            {
                FileSet = new CloudStorageOptions.Types.FileSet
                {
                    Url = gcsUri
                },
                BytesLimitPerFile = 200,
                FileTypes = { new FileType[] { FileType.Csv } },
                FilesLimitPercent = 90,
                SampleMethod = CloudStorageOptions.Types.SampleMethod.RandomStart
            }
        };

        // Construct the Inspect Config and specify the type of info the inspection
        // will look for.
        var inspectConfig = new InspectConfig
        {
            InfoTypes =
            {
                infoTypes ?? new InfoType[] { new InfoType { Name = "PERSON_NAME" } }
            },
            IncludeQuote = true,
            MinLikelihood = minLikelihood
        };

        // Construct the pubsub action.
        var actions = new Action[]
        {
            new Action
            {
                PubSub = new Action.Types.PublishToPubSub
                {
                    Topic = $"projects/{projectId}/topics/{topicId}"
                }
            }
        };

        // Construct the inspect job config using above created objects.
        var inspectJob = new InspectJobConfig
        {
            StorageConfig = storageConfig,
            InspectConfig = inspectConfig,
            Actions = { actions }
        };

        // Issue Create Dlp Job Request
        var request = new CreateDlpJobRequest
        {
            InspectJob = inspectJob,
            ParentAsLocationName = new LocationName(projectId, "global"),
        };

        // We keep the name of the job that we just created.
        var dlpJob = dlp.CreateDlpJob(request);
        var jobName = dlpJob.Name;

        // Listen to pub/sub for the job
        var subscriptionName = new SubscriptionName(projectId, subId);
        var subscriber = await SubscriberClient.CreateAsync(
            subscriptionName);

        await subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == jobName)
            {
                subscriber.StopAsync(cancel);
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        // Get the latest state of the job from the service
        var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
        {
            DlpJobName = DlpJobName.Parse(jobName)
        });

        // Parse the response and process results.
        System.Console.WriteLine($"Job status: {resultJob.State}");
        System.Console.WriteLine($"Job Name: {resultJob.Name}");

        var result = resultJob.InspectDetails.Result;
        foreach (var infoType in result.InfoTypeStats)
        {
            System.Console.WriteLine($"Info Type: {infoType.InfoType.Name}");
            System.Console.WriteLine($"Count: {infoType.Count}");
        }
        return resultJob;
    }
}

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// inspectGcsFileWithSampling inspects a storage with sampling
func inspectGcsFileWithSampling(w io.Writer, projectID, gcsUri, topicID, subscriptionId string) error {
	// projectId := "your-project-id"
	// gcsUri := "gs://" + "your-bucket-name" + "/path/to/your/file.txt"
	// topicID := "your-pubsub-topic-id"
	// subscriptionId := "your-pubsub-subscription-id"

	ctx := context.Background()

	// Initialize a client once and reuse it to send multiple requests. Clients
	// are safe to use across goroutines. When the client is no longer needed,
	// call the Close method to cleanup its resources.
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return err
	}
	// Closing the client safely cleans up background resources.
	defer client.Close()

	// Specify the GCS file to be inspected and sampling configuration
	var cloudStorageOptions = &dlppb.CloudStorageOptions{
		FileSet: &dlppb.CloudStorageOptions_FileSet{
			Url: gcsUri,
		},
		BytesLimitPerFile: int64(200),
		FileTypes: []dlppb.FileType{
			dlppb.FileType_TEXT_FILE,
		},
		FilesLimitPercent: int32(90),
		SampleMethod:      dlppb.CloudStorageOptions_RANDOM_START,
	}

	var storageConfig = &dlppb.StorageConfig{
		Type: &dlppb.StorageConfig_CloudStorageOptions{
			CloudStorageOptions: cloudStorageOptions,
		},
	}

	// Specify the type of info the inspection will look for.
	// See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
	// Specify how the content should be inspected.
	var inspectConfig = &dlppb.InspectConfig{
		InfoTypes: []*dlppb.InfoType{
			{Name: "PERSON_NAME"},
		},
		ExcludeInfoTypes: true,
		IncludeQuote:     true,
		MinLikelihood:    dlppb.Likelihood_POSSIBLE,
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(topicID)
	if exists, err := t.Exists(ctx); err != nil {
		return err
	} else if !exists {
		if t, err = pubsubClient.CreateTopic(ctx, topicID); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(subscriptionId)
	if exists, err := s.Exists(ctx); err != nil {
		return err
	} else if !exists {
		if s, err = pubsubClient.CreateSubscription(ctx, subscriptionId, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + topicID

	var action = &dlppb.Action{
		Action: &dlppb.Action_PubSub{
			PubSub: &dlppb.Action_PublishToPubSub{
				Topic: topic,
			},
		},
	}

	// Configure the long running job we want the service to perform.
	var inspectJobConfig = &dlppb.InspectJobConfig{
		StorageConfig: storageConfig,
		InspectConfig: inspectConfig,
		Actions: []*dlppb.Action{
			action,
		},
	}

	// Create the request for the job configured above.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_InspectJob{
			InspectJob: inspectJobConfig,
		},
	}

	// Use the client to send the request.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Job Created: %v", j.GetName())

	// Wait for the inspect job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()

		// Stop listening for more messages.
		defer cancel()

		resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "Error getting completed job: %v\n", err)
			return
		}
		r := resp.GetInspectDetails().GetResult().GetInfoTypeStats()
		if len(r) == 0 {
			fmt.Fprintf(w, "No results")
			return
		}
		for _, s := range r {
			fmt.Fprintf(w, "\nFound %v instances of infoType %v\n", s.GetCount(), s.GetInfoType().GetName())
		}
	})
	if err != nil {
		return err
	}
	return nil

}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.CloudStorageOptions;
import com.google.privacy.dlp.v2.CloudStorageOptions.FileSet;
import com.google.privacy.dlp.v2.CloudStorageOptions.SampleMethod;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FileType;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InfoTypeStats;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectDataSourceDetails;
import com.google.privacy.dlp.v2.InspectJobConfig;
import com.google.privacy.dlp.v2.Likelihood;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.StorageConfig;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class InspectGcsFileWithSampling {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String gcsUri = "gs://" + "your-bucket-name" + "/path/to/your/file.txt";
    String topicId = "your-pubsub-topic-id";
    String subscriptionId = "your-pubsub-subscription-id";
    inspectGcsFileWithSampling(projectId, gcsUri, topicId, subscriptionId);
  }

  // Inspects a file in a Google Cloud Storage Bucket.
  public static void inspectGcsFileWithSampling(
      String projectId, String gcsUri, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlp = DlpServiceClient.create()) {
      // Specify the GCS file to be inspected and sampling configuration
      CloudStorageOptions cloudStorageOptions =
          CloudStorageOptions.newBuilder()
              .setFileSet(FileSet.newBuilder().setUrl(gcsUri))
              .setBytesLimitPerFile(200)
              .addFileTypes(FileType.TEXT_FILE)
              .setFilesLimitPercent(90)
              .setSampleMethod(SampleMethod.RANDOM_START)
              .build();

      StorageConfig storageConfig =
          StorageConfig.newBuilder().setCloudStorageOptions(cloudStorageOptions).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      InfoType infoType = InfoType.newBuilder().setName("PERSON_NAME").build();

      // Specify how the content should be inspected.
      InspectConfig inspectConfig =
          InspectConfig.newBuilder()
              .addInfoTypes(infoType)
              .setExcludeInfoTypes(true)
              .setIncludeQuote(true)
              .setMinLikelihood(Likelihood.POSSIBLE)
              .build();

      // Specify the action that is triggered when the job completes.
      String pubSubTopic = String.format("projects/%s/topics/%s", projectId, topicId);
      Action.PublishToPubSub publishToPubSub =
          Action.PublishToPubSub.newBuilder().setTopic(pubSubTopic).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the long running job we want the service to perform.
      InspectJobConfig inspectJobConfig =
          InspectJobConfig.newBuilder()
              .setStorageConfig(storageConfig)
              .setInspectConfig(inspectConfig)
              .addActions(action)
              .build();

      // Create the request for the job configured above.
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setInspectJob(inspectJobConfig)
              .build();

      // Use the client to send the request.
      final DlpJob dlpJob = dlp.createDlpJob(createDlpJobRequest);
      System.out.println("Job created: " + dlpJob.getName());

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Get the latest state of the job from the service
      GetDlpJobRequest request = GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();
      DlpJob completedJob = dlp.getDlpJob(request);

      // Parse the response and process results.
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());
      InspectDataSourceDetails.Result result = completedJob.getInspectDetails().getResult();
      System.out.println("Findings: ");
      for (InfoTypeStats infoTypeStat : result.getInfoTypeStatsList()) {
        System.out.print("\tInfo type: " + infoTypeStat.getInfoType().getName());
        System.out.println("\tCount: " + infoTypeStat.getCount());
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The gcs file path
// const gcsUri = 'gs://" + "your-bucket-name" + "/path/to/your/file.txt';

// Specify the type of info the inspection will look for.
// See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
// const infoTypes = [{ name: 'PERSON_NAME' }];

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

// DLP Job max time (in milliseconds)
const DLP_JOB_WAIT_TIME = 15 * 1000 * 60;

async function inspectGcsFileSampling() {
  // Specify the GCS file to be inspected and sampling configuration
  const storageItemConfig = {
    cloudStorageOptions: {
      fileSet: {url: gcsUri},
      bytesLimitPerFile: 200,
      filesLimitPercent: 90,
      fileTypes: [DLP.protos.google.privacy.dlp.v2.FileType.TEXT_FILE],
      sampleMethod:
        DLP.protos.google.privacy.dlp.v2.CloudStorageOptions.SampleMethod
          .RANDOM_START,
    },
  };

  // Specify how the content should be inspected.
  const inspectConfig = {
    infoTypes: infoTypes,
    minLikelihood: DLP.protos.google.privacy.dlp.v2.Likelihood.POSSIBLE,
    includeQuote: true,
    excludeInfoTypes: true,
  };

  // Specify the action that is triggered when the job completes.
  const actions = [
    {
      pubSub: {
        topic: `projects/${projectId}/topics/${topicId}`,
      },
    },
  ];

  // Create the request for the job configured above.
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectJob: {
      inspectConfig: inspectConfig,
      storageConfig: storageItemConfig,
      actions: actions,
    },
  };

  // Use the client to send the request.
  const [topicResponse] = await pubsub.topic(topicId).get();

  // Verify the Pub/Sub topic and listen for job notifications via an
  // existing subscription.
  const subscription = await topicResponse.subscription(subscriptionId);

  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    // Set up the timeout
    const timer = setTimeout(() => {
      reject(new Error('Timeout'));
    }, DLP_JOB_WAIT_TIME);

    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        clearTimeout(timer);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      clearTimeout(timer);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  const [job] = await dlp.getDlpJob({name: jobName});
  console.log(`Job ${job.name} status: ${job.state}`);

  const infoTypeStats = job.inspectDetails.result.infoTypeStats;
  if (infoTypeStats.length > 0) {
    infoTypeStats.forEach(infoTypeStat => {
      console.log(
        `  Found ${infoTypeStat.count} instance(s) of infoType ${infoTypeStat.infoType.name}.`
      );
    });
  } else {
    console.log('No findings.');
  }
}

await inspectGcsFileSampling();

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryOptions\SampleMethod;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CloudStorageOptions;
use Google\Cloud\Dlp\V2\CloudStorageOptions\FileSet;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectJobConfig;
use Google\Cloud\Dlp\V2\StorageConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Inspect storage with sampling.
 * The following examples demonstrate using the Cloud DLP API to scan a 90% subset of a
 * Cloud Storage bucket for person names. The scan starts from a random location in the dataset
 * and only includes text files under 200 bytes.
 *
 * @param string $callingProjectId  The project ID to run the API call under.
 * @param string $gcsUri            Google Cloud Storage file url.
 * @param string $topicId           The ID of the Pub/Sub topic to notify once the job completes.
 * @param string $subscriptionId    The ID of the Pub/Sub subscription to use when listening for job.
 */
function inspect_gcs_with_sampling(
    // TODO(developer): Replace sample parameters before running the code.
    string $callingProjectId,
    string $gcsUri = 'gs://GOOGLE_STORAGE_BUCKET_NAME/dlp_sample.csv',
    string $topicId = 'dlp-pubsub-topic',
    string $subscriptionId = 'dlp_subcription'
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Construct the items to be inspected.
    $cloudStorageOptions = (new CloudStorageOptions())
        ->setFileSet((new FileSet())
            ->setUrl($gcsUri))
        ->setBytesLimitPerFile(200)
        ->setFilesLimitPercent(90)
        ->setSampleMethod(SampleMethod::RANDOM_START);

    $storageConfig = (new StorageConfig())
        ->setCloudStorageOptions($cloudStorageOptions);

    // Specify the type of info the inspection will look for.
    $phoneNumberInfoType = (new InfoType())
        ->setName('PHONE_NUMBER');
    $emailAddressInfoType = (new InfoType())
        ->setName('EMAIL_ADDRESS');
    $cardNumberInfoType = (new InfoType())
        ->setName('CREDIT_CARD_NUMBER');
    $infoTypes = [$phoneNumberInfoType, $emailAddressInfoType, $cardNumberInfoType];

    // Specify how the content should be inspected.
    $inspectConfig = (new InspectConfig())
        ->setInfoTypes($infoTypes)
        ->setIncludeQuote(true);

    // Construct the action to run when job completes.
    $action = (new Action())
        ->setPubSub((new PublishToPubSub())
            ->setTopic($topic->name()));

    // Construct inspect job config to run.
    $inspectJob = (new InspectJobConfig())
        ->setInspectConfig($inspectConfig)
        ->setStorageConfig($storageConfig)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request.
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setInspectJob($inspectJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes.
    // Consider using an asynchronous execution model such as Cloud Functions.
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while.
            }
        }
        printf('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds.
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout.

    // Print finding counts.
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $infoTypeStats = $job->getInspectDetails()->getResult()->getInfoTypeStats();
            if (count($infoTypeStats) === 0) {
                printf('No findings.' . PHP_EOL);
            } else {
                foreach ($infoTypeStats as $infoTypeStat) {
                    printf(
                        '  Found %s instance(s) of infoType %s' . PHP_EOL,
                        $infoTypeStat->getCount(),
                        $infoTypeStat->getInfoType()->getName()
                    );
                }
            }
            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            printf('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            printf('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import threading
from typing import List

import google.cloud.dlp
import google.cloud.pubsub


def inspect_gcs_with_sampling(
    project: str,
    bucket: str,
    topic_id: str,
    subscription_id: str,
    info_types: List[str] = None,
    file_types: List[str] = None,
    min_likelihood: str = None,
    max_findings: int = None,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to analyze files in GCS by
    limiting the amount of data to be scanned.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        bucket: The name of the GCS bucket containing the file, as a string.
        topic_id: The id of the Cloud Pub/Sub topic to which the API will
            broadcast job completion. The topic must already exist.
        subscription_id: The id of the Cloud Pub/Sub subscription to listen on
            while waiting for job completion. The subscription must already
            exist and be subscribed to the topic.
        info_types: A list of strings representing infoTypes to look for.
            A full list of info type categories can be fetched from the API.
        file_types: Type of files in gcs bucket where the inspection would happen.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        timeout: The number of seconds to wait for a response from the API.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries.
    if not info_types:
        info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
    info_types = [{"name": info_type} for info_type in info_types]

    # Specify how the content should be inspected. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": info_types,
        "exclude_info_types": True,
        "include_quote": True,
        "min_likelihood": min_likelihood,
        "limits": {"max_findings_per_request": max_findings},
    }

    # Setting default file types as CSV files
    if not file_types:
        file_types = ["CSV"]

    # Construct a cloud_storage_options dictionary with the bucket's URL.
    url = f"gs://{bucket}/*"
    storage_config = {
        "cloud_storage_options": {
            "file_set": {"url": url},
            "bytes_limit_per_file": 200,
            "file_types": file_types,
            "files_limit_percent": 90,
            "sample_method": "RANDOM_START",
        }
    }

    # Tell the API where to send a notification when the job is complete.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    actions = [{"pub_sub": {"topic": topic}}]

    # Construct the inspect_job, which defines the entire inspect content task.
    inspect_job = {
        "inspect_config": inspect_config,
        "storage_config": storage_config,
        "actions": actions,
    }

    # Convert the project id into full resource ids.
    parent = f"projects/{project}/locations/global"

    # Call the API
    operation = dlp.create_dlp_job(
        request={"parent": parent, "inspect_job": inspect_job}
    )
    print(f"Inspection operation started: {operation.name}")

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)

    # Set up a callback to acknowledge a message. This closes around an event
    # so that it can signal that it is done and the main thread can continue.
    job_done = threading.Event()

    def callback(message):
        try:
            if message.attributes["DlpJobName"] == operation.name:
                # This is the message we're looking for, so acknowledge it.
                message.ack()

                # Now that the job is done, fetch the results and print them.
                job = dlp.get_dlp_job(request={"name": operation.name})
                print(f"Job name: {job.name}")
                if job.inspect_details.result.info_type_stats:
                    print("Findings:")
                    for finding in job.inspect_details.result.info_type_stats:
                        print(
                            f"Info type: {finding.info_type.name}; Count: {finding.count}"
                        )
                else:
                    print("No findings.")

                # Signal to the main thread that we can exit.
                job_done.set()
            else:
                # This is not the message we're looking for.
                message.drop()
        except Exception as e:
            # Because this is executing in a thread, an exception won't be
            # noted unless we print it manually.
            print(e)
            raise

    # Register the callback and wait on the event.
    subscriber.subscribe(subscription_path, callback=callback)
    finished = job_done.wait(timeout=timeout)
    if not finished:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )

REST

JSON 輸入：

POST https://dlp.googleapis.com/v2/projects/[PROJECT-ID]/dlpJobs?key={YOUR_API_KEY}

{
  "inspectJob":{
    "storageConfig":{
      "cloudStorageOptions":{
        "fileSet":{
          "url":"gs://[BUCKET-NAME]/*"
        },
        "bytesLimitPerFile":"200",
        "fileTypes":[
          "TEXT_FILE"
        ],
        "filesLimitPercent":90,
        "sampleMethod":"RANDOM_START"
      }
    },
    "inspectConfig":{
      "infoTypes":[
        {
          "name":"PERSON_NAME"
        }
      ],
      "excludeInfoTypes":true,
      "includeQuote":true,
      "minLikelihood":"POSSIBLE"
    },
    "actions":[
      {
        "saveFindings":{
          "outputConfig":{
            "table":{
              "projectId":"[PROJECT-ID]",
              "datasetId":"testingdlp"
            },
            "outputSchema":"BASIC_COLUMNS"
          }
        }
      }
    ]
  }
}

在 POST 要求中將 JSON 輸入內容傳送至指定端點後，系統會建立 Sensitive Data Protection 工作，API 則會傳送下列回應。

JSON 輸出內容：

{
  "name":"projects/[PROJECT-ID]/dlpJobs/[JOB-ID]",
  "type":"INSPECT_JOB",
  "state":"PENDING",
  "inspectDetails":{
    "requestedOptions":{
      "snapshotInspectTemplate":{

      },
      "jobConfig":{
        "storageConfig":{
          "cloudStorageOptions":{
            "fileSet":{
              "url":"gs://[BUCKET_NAME]/*"
            },
            "bytesLimitPerFile":"200",
            "fileTypes":[
              "TEXT_FILE"
            ],
            "sampleMethod":"TOP",
            "filesLimitPercent":90
          }
        },
        "inspectConfig":{
          "infoTypes":[
            {
              "name":"PERSON_NAME"
            }
          ],
          "minLikelihood":"POSSIBLE",
          "limits":{

          },
          "includeQuote":true,
          "excludeInfoTypes":true
        },
        "actions":[
          {
            "saveFindings":{
              "outputConfig":{
                "table":{
                  "projectId":"[PROJECT-ID]",
                  "datasetId":"[DATASET-ID]",
                  "tableId":"[TABLE-ID]"
                },
                "outputSchema":"BASIC_COLUMNS"
              }
            }
          }
        ]
      }
    }
  },
  "createTime":"2018-05-30T22:22:08.279Z"
}

限制 BigQuery 掃描

如要在 BigQuery 中啟用取樣功能，限制掃描的資料量，請在 BigQueryOptions 中指定下列選用欄位：

• rowsLimit：要掃描的資料列數量上限。如果表格有比這個值更多的資料列，則會省略其餘的資料列。如未設定，或已設定為 0，則會掃描所有資料列。

• rowsLimitPercent：要掃描的列數百分比上限 (介於 0 和 100 之間)。其餘資料列則會省略。將這個值設為 0 或 100，表示沒有限制。預設值為 0。只能指定 rowsLimit 和 rowsLimitPercent 其中之一。

  注意： 已知問題導致 rowsLimitPercent 欄位出現非預期行為。建議改用 rowsLimit。

• sampleMethod：如果未掃描所有資料列，如何取樣資料列。如未指定，掃描會從頂端開始。這個欄位可設為下列其中一個值：

  • TOP：掃描會從頂端開始。
  • RANDOM_START：掃描會從隨機選取的資料列開始。

• excludedFields：資料表欄位，可明確識別要排除讀取的資料欄。這有助於減少掃描的資料量，並降低檢查工作的整體成本。

• includedFields：資料表欄位，可專屬識別要掃描的資料表內特定資料列。

另一項有助於限制掃描資料量的功能是 TimespanConfig，特別是在掃描分區資料表時。TimespanConfig 可讓您提供開始和結束時間值來定義時間範圍，藉此篩除 BigQuery 資料表列。Sensitive Data Protection 則只會掃描時間戳記落在該時間範圍內的資料列。

下列範例示範如何使用 DLP API 掃描 BigQuery 資料表中 1000 列的子集。掃描會從隨機列開始。

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// inspectBigQueryTableWithSampling inspect bigQueries for sensitive data with sampling
func inspectBigQueryTableWithSampling(w io.Writer, projectID, topicID, subscriptionID string) error {
	// projectId := "your-project-id"
	// topicID := "your-pubsub-topic-id"
	// or provide a topicID name to create one
	// subscriptionID := "your-pubsub-subscription-id"
	// or provide a subscription name to create one

	ctx := context.Background()

	// Initialize a client once and reuse it to send multiple requests. Clients
	// are safe to use across goroutines. When the client is no longer needed,
	// call the Close method to cleanup its resources.
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return err
	}

	// Closing the client safely cleans up background resources.
	defer client.Close()

	// Specify the BigQuery table to be inspected.
	tableReference := &dlppb.BigQueryTable{
		ProjectId: "bigquery-public-data",
		DatasetId: "usa_names",
		TableId:   "usa_1910_current",
	}

	bigQueryOptions := &dlppb.BigQueryOptions{
		TableReference: tableReference,
		RowsLimit:      int64(10000),
		SampleMethod:   dlppb.BigQueryOptions_RANDOM_START,
		IdentifyingFields: []*dlppb.FieldId{
			{Name: "name"},
		},
	}

	// Provide storage config with BigqueryOptions
	storageConfig := &dlppb.StorageConfig{
		Type: &dlppb.StorageConfig_BigQueryOptions{
			BigQueryOptions: bigQueryOptions,
		},
	}

	// Specify the type of info the inspection will look for.
	// See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
	infoTypes := []*dlppb.InfoType{
		{Name: "PERSON_NAME"},
	}

	// Specify how the content should be inspected.
	inspectConfig := &dlppb.InspectConfig{
		InfoTypes:    infoTypes,
		IncludeQuote: true,
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(topicID)
	if exists, err := t.Exists(ctx); err != nil {
		return err
	} else if !exists {
		if t, err = pubsubClient.CreateTopic(ctx, topicID); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(subscriptionID)
	if exists, err := s.Exists(ctx); err != nil {
		return err
	} else if !exists {
		if s, err = pubsubClient.CreateSubscription(ctx, subscriptionID, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := fmt.Sprintf("projects/%s/topics/%s", projectID, topicID)

	action := &dlppb.Action{
		Action: &dlppb.Action_PubSub{
			PubSub: &dlppb.Action_PublishToPubSub{
				Topic: topic,
			},
		},
	}

	// Configure the long running job we want the service to perform.
	inspectJobConfig := &dlppb.InspectJobConfig{
		StorageConfig: storageConfig,
		InspectConfig: inspectConfig,
		Actions: []*dlppb.Action{
			action,
		},
	}

	// Create the request for the job configured above.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_InspectJob{
			InspectJob: inspectJobConfig,
		},
	}

	// Use the client to send the request.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Job Created: %v", j.GetName())

	// Wait for the inspect job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	c, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(c, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()

		// Stop listening for more messages.
		defer cancel()
	})
	if err != nil {
		return err
	}

	resp, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
		Name: j.GetName(),
	})
	if err != nil {
		return err
	}
	r := resp.GetInspectDetails().GetResult().GetInfoTypeStats()
	if len(r) == 0 {
		fmt.Fprintf(w, "No results")
		return err
	}
	for _, s := range r {
		fmt.Fprintf(w, "\nFound %v instances of infoType %v\n", s.GetCount(), s.GetInfoType().GetName())
	}
	return nil

}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.BigQueryOptions;
import com.google.privacy.dlp.v2.BigQueryOptions.SampleMethod;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InfoTypeStats;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectDataSourceDetails;
import com.google.privacy.dlp.v2.InspectJobConfig;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.StorageConfig;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class InspectBigQueryTableWithSampling {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String topicId = "your-pubsub-topic-id";
    String subscriptionId = "your-pubsub-subscription-id";
    inspectBigQueryTableWithSampling(projectId, topicId, subscriptionId);
  }

  // Inspects a BigQuery Table
  public static void inspectBigQueryTableWithSampling(
      String projectId, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlp = DlpServiceClient.create()) {
      // Specify the BigQuery table to be inspected.
      BigQueryTable tableReference =
          BigQueryTable.newBuilder()
              .setProjectId("bigquery-public-data")
              .setDatasetId("usa_names")
              .setTableId("usa_1910_current")
              .build();

      BigQueryOptions bigQueryOptions =
          BigQueryOptions.newBuilder()
              .setTableReference(tableReference)
              .setRowsLimit(1000)
              .setSampleMethod(SampleMethod.RANDOM_START)
              .addIdentifyingFields(FieldId.newBuilder().setName("name"))
              .build();

      StorageConfig storageConfig =
          StorageConfig.newBuilder().setBigQueryOptions(bigQueryOptions).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      InfoType infoType = InfoType.newBuilder().setName("PERSON_NAME").build();

      // Specify how the content should be inspected.
      InspectConfig inspectConfig =
          InspectConfig.newBuilder().addInfoTypes(infoType).setIncludeQuote(true).build();

      // Specify the action that is triggered when the job completes.
      String pubSubTopic = String.format("projects/%s/topics/%s", projectId, topicId);
      Action.PublishToPubSub publishToPubSub =
          Action.PublishToPubSub.newBuilder().setTopic(pubSubTopic).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the long running job we want the service to perform.
      InspectJobConfig inspectJobConfig =
          InspectJobConfig.newBuilder()
              .setStorageConfig(storageConfig)
              .setInspectConfig(inspectConfig)
              .addActions(action)
              .build();

      // Create the request for the job configured above.
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setInspectJob(inspectJobConfig)
              .build();

      // Use the client to send the request.
      final DlpJob dlpJob = dlp.createDlpJob(createDlpJobRequest);
      System.out.println("Job created: " + dlpJob.getName());

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Get the latest state of the job from the service
      GetDlpJobRequest request = GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();
      DlpJob completedJob = dlp.getDlpJob(request);

      // Parse the response and process results.
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());
      InspectDataSourceDetails.Result result = completedJob.getInspectDetails().getResult();
      System.out.println("Findings: ");
      for (InfoTypeStats infoTypeStat : result.getInfoTypeStatsList()) {
        System.out.print("\tInfo type: " + infoTypeStat.getInfoType().getName());
        System.out.println("\tCount: " + infoTypeStat.getCount());
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const dataProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

// DLP Job max time (in milliseconds)
const DLP_JOB_WAIT_TIME = 15 * 1000 * 60;

async function inspectBigqueryWithSampling() {
  // Specify the type of info the inspection will look for.
  // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
  const infoTypes = [{name: 'PERSON_NAME'}];

  // Specify the BigQuery options required for inspection.
  const storageItem = {
    bigQueryOptions: {
      tableReference: {
        projectId: dataProjectId,
        datasetId: datasetId,
        tableId: tableId,
      },
      rowsLimit: 1000,
      sampleMethod:
        DLP.protos.google.privacy.dlp.v2.BigQueryOptions.SampleMethod
          .RANDOM_START,
      includedFields: [{name: 'name'}],
    },
  };

  // Specify the action that is triggered when the job completes.
  const actions = [
    {
      pubSub: {
        topic: `projects/${projectId}/topics/${topicId}`,
      },
    },
  ];

  // Construct request for creating an inspect job
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectJob: {
      inspectConfig: {
        infoTypes: infoTypes,
        includeQuote: true,
      },
      storageConfig: storageItem,
      actions: actions,
    },
  };
  // Use the client to send the request.
  const [topicResponse] = await pubsub.topic(topicId).get();

  // Verify the Pub/Sub topic and listen for job notifications via an
  // existing subscription.
  const subscription = await topicResponse.subscription(subscriptionId);

  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;

  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    // Set up the timeout
    const timer = setTimeout(() => {
      reject(new Error('Timeout'));
    }, DLP_JOB_WAIT_TIME);

    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        clearTimeout(timer);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      clearTimeout(timer);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  const [job] = await dlp.getDlpJob({name: jobName});
  console.log(`Job ${job.name} status: ${job.state}`);

  const infoTypeStats = job.inspectDetails.result.infoTypeStats;
  if (infoTypeStats.length > 0) {
    infoTypeStats.forEach(infoTypeStat => {
      console.log(
        `  Found ${infoTypeStat.count} instance(s) of infoType ${infoTypeStat.infoType.name}.`
      );
    });
  } else {
    console.log('No findings.');
  }
}

await inspectBigqueryWithSampling();

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryOptions;
use Google\Cloud\Dlp\V2\BigQueryOptions\SampleMethod;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectJobConfig;
use Google\Cloud\Dlp\V2\StorageConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Inspect BigQuery for sensitive data with sampling.
 * The following examples demonstrate using the Cloud Data Loss Prevention
 * API to scan a 1000-row subset of a BigQuery table. The scan starts from
 * a random row.
 *
 * @param string $callingProjectId  The project ID to run the API call under.
 * @param string $topicId           The Pub/Sub topic ID to notify once the job is completed.
 * @param string $subscriptionId    The Pub/Sub subscription ID to use when listening for job.
 * @param string $projectId         The Google Cloud Project ID.
 * @param string $datasetId         The BigQuery Dataset ID.
 * @param string $tableId           The BigQuery Table ID to be inspected.
 */
function inspect_bigquery_with_sampling(
    string $callingProjectId,
    string $topicId,
    string $subscriptionId,
    string $projectId,
    string $datasetId,
    string $tableId
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Specify the BigQuery table to be inspected.
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($projectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    $bigQueryOptions = (new BigQueryOptions())
        ->setTableReference($bigqueryTable)
        ->setRowsLimit(1000)
        ->setSampleMethod(SampleMethod::RANDOM_START)
        ->setIdentifyingFields([
            (new FieldId())
                ->setName('name')
        ]);

    $storageConfig = (new StorageConfig())
        ->setBigQueryOptions($bigQueryOptions);

    // Specify the type of info the inspection will look for.
    // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
    $personNameInfoType = (new InfoType())
        ->setName('PERSON_NAME');
    $infoTypes = [$personNameInfoType];

    // Specify how the content should be inspected.
    $inspectConfig = (new InspectConfig())
        ->setInfoTypes($infoTypes)
        ->setIncludeQuote(true);

    // Specify the action that is triggered when the job completes.
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Configure the long running job we want the service to perform.
    $inspectJob = (new InspectJobConfig())
        ->setInspectConfig($inspectConfig)
        ->setStorageConfig($storageConfig)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setInspectJob($inspectJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        printf('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $infoTypeStats = $job->getInspectDetails()->getResult()->getInfoTypeStats();
            if (count($infoTypeStats) === 0) {
                printf('No findings.' . PHP_EOL);
            } else {
                foreach ($infoTypeStats as $infoTypeStat) {
                    printf(
                        '  Found %s instance(s) of infoType %s' . PHP_EOL,
                        $infoTypeStat->getCount(),
                        $infoTypeStat->getInfoType()->getName()
                    );
                }
            }
            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            printf('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            printf('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import threading

import google.cloud.dlp
import google.cloud.pubsub


def inspect_bigquery_table_with_sampling(
    project: str,
    topic_id: str,
    subscription_id: str,
    min_likelihood: str = None,
    max_findings: str = None,
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to analyze BigQuery data by limiting
    the amount of data to be scanned.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        topic_id: The id of the Cloud Pub/Sub topic to which the API will
            broadcast job completion. The topic must already exist.
        subscription_id: The id of the Cloud Pub/Sub subscription to listen on
            while waiting for job completion. The subscription must already
            exist and be subscribed to the topic.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        timeout: The number of seconds to wait for a response from the API.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Specify how the content should be inspected. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": [{"name": "PERSON_NAME"}],
        "min_likelihood": min_likelihood,
        "limits": {"max_findings_per_request": max_findings},
        "include_quote": True,
    }

    # Specify the BigQuery table to be inspected.
    # Here we are using public bigquery table.
    table_reference = {
        "project_id": "bigquery-public-data",
        "dataset_id": "usa_names",
        "table_id": "usa_1910_current",
    }

    # Construct a storage_config containing the target BigQuery info.
    storage_config = {
        "big_query_options": {
            "table_reference": table_reference,
            "rows_limit": 1000,
            "sample_method": "RANDOM_START",
            "identifying_fields": [{"name": "name"}],
        }
    }

    # Tell the API where to send a notification when the job is complete.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    actions = [{"pub_sub": {"topic": topic}}]

    # Construct the inspect_job, which defines the entire inspect content task.
    inspect_job = {
        "inspect_config": inspect_config,
        "storage_config": storage_config,
        "actions": actions,
    }

    # Convert the project id into full resource ids.
    parent = f"projects/{project}/locations/global"

    # Call the API
    operation = dlp.create_dlp_job(
        request={"parent": parent, "inspect_job": inspect_job}
    )
    print(f"Inspection operation started: {operation.name}")

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)

    # Set up a callback to acknowledge a message. This closes around an event
    # so that it can signal that it is done and the main thread can continue.
    job_done = threading.Event()

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        try:
            if message.attributes["DlpJobName"] == operation.name:
                # This is the message we're looking for, so acknowledge it.
                message.ack()

                # Now that the job is done, fetch the results and print them.
                job = dlp.get_dlp_job(request={"name": operation.name})
                print(f"Job name: {job.name}")

                if job.inspect_details.result.info_type_stats:
                    for finding in job.inspect_details.result.info_type_stats:
                        print(
                            f"Info type: {finding.info_type.name}; Count: {finding.count}"
                        )
                else:
                    print("No findings.")

                # Signal to the main thread that we can exit.
                job_done.set()
            else:
                # This is not the message we're looking for.
                message.drop()

        except Exception as e:
            # Because this is executing in a thread, an exception won't be
            # noted unless we print it manually.
            print(e)
            raise

    # Register the callback and wait on the event.
    subscriber.subscribe(subscription_path, callback=callback)
    finished = job_done.wait(timeout=timeout)
    if not finished:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.InspectConfig.Types;

public class InspectBigQueryWithSampling
{
    public static async Task<DlpJob> InspectAsync(
        string projectId,
        int maxFindings,
        bool includeQuote,
        string topicId,
        string subId,
        Likelihood minLikelihood = Likelihood.Possible,
        IEnumerable<FieldId> identifyingFields = null,
        IEnumerable<InfoType> infoTypes = null)
    {

        // Instantiate the dlp client.
        var dlp = DlpServiceClient.Create();

        // Construct Storage config.
        var storageConfig = new StorageConfig
        {
            BigQueryOptions = new BigQueryOptions
            {
                TableReference = new BigQueryTable
                {
                    ProjectId = "bigquery-public-data",
                    DatasetId = "usa_names",
                    TableId = "usa_1910_current",
                },
                IdentifyingFields =
                {
                    identifyingFields ?? new FieldId[] { new FieldId { Name = "name" } }
                },
                RowsLimit = 100,
                SampleMethod = BigQueryOptions.Types.SampleMethod.RandomStart
            }
        };

        // Construct the inspect config.
        var inspectConfig = new InspectConfig
        {
            InfoTypes = { infoTypes ?? new InfoType[] { new InfoType { Name = "PERSON_NAME" } } },
            Limits = new FindingLimits
            {
                MaxFindingsPerRequest = maxFindings,
            },
            IncludeQuote = includeQuote,
            MinLikelihood = minLikelihood
        };

        // Construct the pubsub action.
        var actions = new Action[]
        {
            new Action
            {
                PubSub = new Action.Types.PublishToPubSub
                {
                    Topic = $"projects/{projectId}/topics/{topicId}"
                }
            }
        };

        // Construct the inspect job config using the actions.
        var inspectJob = new InspectJobConfig
        {
            StorageConfig = storageConfig,
            InspectConfig = inspectConfig,
            Actions = { actions }
        };

        // Issue Create Dlp Job Request.
        var request = new CreateDlpJobRequest
        {
            InspectJob = inspectJob,
            ParentAsLocationName = new LocationName(projectId, "global"),
        };

        // We keep the name of the job that we just created.
        var dlpJob = dlp.CreateDlpJob(request);
        var jobName = dlpJob.Name;

        // Listen to pub/sub for the job.
        var subscriptionName = new SubscriptionName(projectId, subId);
        var subscriber = await SubscriberClient.CreateAsync(
            subscriptionName);

        // SimpleSubscriber runs your message handle function on multiple threads to maximize throughput.
        await subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == jobName)
            {
                subscriber.StopAsync(cancel);
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        // Get the latest state of the job from the service.
        var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
        {
            DlpJobName = DlpJobName.Parse(jobName)
        });

        // Parse the response and process results.
        System.Console.WriteLine($"Job status: {resultJob.State}");
        System.Console.WriteLine($"Job Name: {resultJob.Name}");
        var result = resultJob.InspectDetails.Result;
        foreach (var infoType in result.InfoTypeStats)
        {
            System.Console.WriteLine($"Info Type: {infoType.InfoType.Name}");
            System.Console.WriteLine($"Count: {infoType.Count}");
        }
        return resultJob;
    }
}

REST

JSON 輸入：

POST https://dlp.googleapis.com/v2/projects/[PROJECT-ID]/dlpJobs?key={YOUR_API_KEY}

{
  "inspectJob":{
    "storageConfig":{
      "bigQueryOptions":{
        "tableReference":{
          "projectId":"bigquery-public-data",
          "datasetId":"usa_names",
          "tableId":"usa_1910_current"
        },
        "rowsLimit":"1000",
        "sampleMethod":"RANDOM_START",
        "includedFields":[
          {
            "name":"name"
          }
        ]
      }
    },
    "inspectConfig":{
      "infoTypes":[
        {
          "name":"FIRST_NAME"
        }
      ],
      "includeQuote":true
    },
    "actions":[
      {
        "saveFindings":{
          "outputConfig":{
            "table":{
              "projectId":"[PROJECT-ID]",
              "datasetId":"testingdlp",
              "tableId":"bqsample3"
            },
            "outputSchema":"BASIC_COLUMNS"
          }
        }
      }
    ]
  }
}

在 POST 要求中將 JSON 輸入內容傳送至指定端點後，系統會建立 Sensitive Data Protection 工作，API 則會傳送下列回應。

JSON 輸出內容：

{
  "name": "projects/[PROJECT-ID]/dlpJobs/[JOB-ID]",
  "type": "INSPECT_JOB",
  "state": "PENDING",
  "inspectDetails": {
    "requestedOptions": {
      "snapshotInspectTemplate": {},
      "jobConfig": {
        "storageConfig": {
          "bigQueryOptions": {
            "tableReference": {
              "projectId": "bigquery-public-data",
              "datasetId": "usa_names",
              "tableId": "usa_1910_current"
            },
            "rowsLimit": "1000",
            "sampleMethod": "RANDOM_START",
            "includedFields": [
              {
                "name": "name"
              }
            ]
          }
        },
        "inspectConfig": {
          "infoTypes": [
            {
              "name": "FIRST_NAME"
            }
          ],
          "limits": {},
          "includeQuote": true
        },
        "actions": [
          {
            "saveFindings": {
              "outputConfig": {
                "table": {
                  "projectId": "[PROJECT-ID]",
                  "datasetId": "[DATASET-ID]",
                  "tableId": "bqsample"
                },
                "outputSchema": "BASIC_COLUMNS"
              }
            }
          }
        ]
      }
    },
    "result": {}
  },
  "createTime": "2022-11-04T18:53:48.350Z"
}

檢查工作執行完畢，且 BigQuery 處理完結果後，掃描結果就會顯示在指定的 BigQuery 輸出資料表中。如要進一步瞭解如何擷取檢查結果，請參閱下一節。

擷取檢查結果

注意： 本節說明如何使用 DLP API 查看檢查結果。如要在Google Cloud 控制台中使用 Sensitive Data Protection 查看結果，請依序點選「Jobs & job triggers」(工作和工作觸發條件) 分頁標籤和「All jobs」(所有工作) 分頁標籤，然後點選要查看結果的工作。

您可以使用 projects.dlpJobs.get 方法擷取 DlpJob 的摘要。傳回的 DlpJob 包含 InspectDataSourceDetails 物件，其中包含工作設定的摘要 (RequestedOptions) 和工作結果的摘要 (Result)。結果摘要包括：

• processedBytes：已處理的總大小 (以位元組為單位)。
• totalEstimatedBytes：預估剩餘待處理的位元組數。
• InfoTypeStatistics 物件：檢查工作中找到的各個 infoType 例項數量統計資料。

如要查看完整的檢查工作結果，可以採取以下任一做法：檢查工作的執行方式取決於您選擇的Action：

• 儲存至 BigQuery (SaveFindings 物件)，並存放在指定的資料表中。查看或分析結果前，請先使用 projects.dlpJobs.get 方法 (如下所述)，確認工作已完成。請注意，您可以使用 OutputSchema 物件指定儲存發現項目的結構定義。
• 發布至 Pub/Sub 主題 (即 PublishToPubSub 物件)。主題必須將發布存取權授予執行 DlpJob 傳送通知的 Sensitive Data Protection 服務帳戶。
• 已發布至 Security Command Center。
• 發布至 Cloud Monitoring。

為協助篩選 Sensitive Data Protection 產生的大量資料，您可以使用內建的 BigQuery 工具執行豐富的 SQL 分析，或使用數據分析等工具產生報表。詳情請參閱「分析及回報 Sensitive Data Protection 發現項目」。如需查詢範例，請參閱「在 BigQuery 中查詢調查結果」。

將儲存空間存放區檢查要求傳送至 Sensitive Data Protection，會建立並執行 DlpJob 物件執行個體做為回應。視您的資料大小及指定的設定而定，執行這些工作可能需要幾秒、幾分或幾小時。如果選擇發布至 Pub/Sub 主題 (在 Action 中指定 PublishToPubSub)，系統會在工作狀態變更時，自動將通知傳送至指定名稱的主題。Pub/Sub 主題名稱的指定格式為 projects/[PROJECT-ID]/topics/[PUBSUB-TOPIC-NAME]。

注意： 如要進一步瞭解如何建立 Pub/Sub 函式，請參閱 Pub/Sub 教學課程。

您可以使用以下管理方法充分控管自己建立的工作：

• projects.dlpJobs.cancel method：停止目前正在進行的工作。伺服器會盡力取消工作，但不保證一定會成功。工作和設定會保留到您刪除為止 (使用
• projects.dlpJobs.delete 方法：刪除工作及其設定。
• projects.dlpJobs.get 方法：擷取單一工作，並傳回其狀態、設定，以及摘要結果 (如果工作已完成)。
• projects.dlpJobs.list 方法：擷取所有工作的清單，並提供篩選結果的功能。

後續步驟

• 如要進一步瞭解如何建立儲存空間檢查工作，請參閱「建立及排定 Sensitive Data Protection 檢查工作」。
• 進一步瞭解如何在儲存空間建立去識別化的資料副本。
• 如要進一步瞭解檢查 Cloud Storage bucket 時支援的檔案類型，請參閱「支援的檔案類型」。