Collect Cisco CloudLock CASB logs

Supported in:

Google secops SIEM

This document explains how to ingest Cisco CloudLock CASB logs to Google Security Operations using Google Cloud Storage. A Cloud Run function polls the Cisco CloudLock API on a schedule and writes incident, activity, and entity data to a GCS bucket, which Google SecOps then ingests via a GCS V2 feed.

Cisco CloudLock is a cloud-native Cloud Access Security Broker (CASB) that provides visibility and control over cloud applications. It helps organizations discover shadow IT, enforce data loss prevention policies, detect threats, and maintain compliance across SaaS applications.

Before you begin

Make sure that you have the following prerequisites:

A Google SecOps instance
A GCP project with Cloud Storage API enabled
Permissions to create and manage GCS buckets
Permissions to manage IAM policies on GCS buckets
Permissions to create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
Privileged access to Cisco CloudLock admin console
Cisco CloudLock API Base URL (provided by Cisco CloudLock Support)

Get Cisco CloudLock API credentials

To get started, contact Cisco CloudLock Support to obtain your CloudLock API URL. The API base URL is specific to your tenant and is not publicly available. Generate an access token in the CloudLock application by navigating to the Authentication & API tab in the Settings page.

Sign in to the Cisco CloudLock admin console.
Go to Settings > Authentication & API.
Under API, click Generate to create your access token.
Copy and save the following details in a secure location:
- API Access Token: The Bearer token displayed after clicking Generate.
- API Base URL: Provided by Cisco CloudLock Support. The format is https://{YourCloudlockAPIServer}.
Note: Only one API token can be generated at a time per user, per CloudLock environment. Generating a new token invalidates any previously generated token.
Note: API keys, passwords, secrets, and tokens allow access to your private customer data and should never be shared with another user or organization.

Verify API access

Test your credentials before proceeding with the integration:

CLOUDLOCK_API_TOKEN="your-api-token"
CLOUDLOCK_API_BASE="https://your-cloudlock-api-server"

curl -v -H "Authorization: Bearer ${CLOUDLOCK_API_TOKEN}" \
  "${CLOUDLOCK_API_BASE}/api/v2/incidents?limit=1"

A successful response returns HTTP 200 with a JSON object containing an items array. If you receive HTTP 401, verify the API token. If you receive HTTP 403, confirm the account has the required permissions.

Create Google Cloud Storage bucket

Go to the Google Cloud Console.
Select your project or create a new one.
In the navigation menu, go to Cloud Storage > Buckets.
Click Create bucket.

Provide the following configuration details:

Setting	Value
Name your bucket	Enter a globally unique name (for example, `cisco-cloudlock-logs`)
Location type	Choose based on your needs (Region, Dual-region, Multi-region)
Location	Select the location (for example, `us-central1`)
Storage class	Standard (recommended for frequently accessed logs)
Access control	Uniform (recommended)
Protection tools	Optional: Enable object versioning or retention policy

Click Create.

Create service account for Cloud Run function

The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.

Create service account

In the GCP Console, go to IAM & Admin > Service Accounts.
Click Create Service Account.
Provide the following configuration details:
- Service account name: Enter cloudlock-data-export-sa.
- Service account description: Enter Service account for Cloud Run function to collect Cisco CloudLock logs.
Click Create and Continue.
In the Grant this service account access to project section:
1. Click Select a role.
2. Search for and select Storage Object Admin.
3. Click + Add another role.
4. Search for and select Cloud Run Invoker.
5. Click + Add another role.
6. Search for and select Cloud Functions Invoker.
Click Continue.
Click Done.

These roles are required for:

Storage Object Admin: Write logs to GCS bucket and manage state files
Cloud Run Invoker: Allow Pub/Sub to invoke the function
Cloud Functions Invoker: Allow function invocation

Grant IAM permissions on GCS bucket

Grant the service account write permissions on the GCS bucket:

Go to Cloud Storage > Buckets.
Click your bucket name.
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Enter the service account email (for example, cloudlock-data-export-sa@PROJECT_ID.iam.gserviceaccount.com).
- Assign roles: Select Storage Object Admin.
Click Save.

Create Pub/Sub topic

Create a Pub/Sub topic that Cloud Scheduler will publish to and the Cloud Run function will subscribe to.

In the GCP Console, go to Pub/Sub > Topics.
Click Create topic.
Provide the following configuration details:
- Topic ID: Enter cloudlock-data-export-trigger.
- Leave other settings as default.
Click Create.

Create Cloud Run function to collect logs

The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from Cisco CloudLock API and write them to GCS.

In the GCP Console, go to Cloud Run.
Click Create service.
Select Function (use an inline editor to create a function).
In the Configure section, provide the following configuration details:

Setting Value

Service name cloudlock-data-export

Region Select region matching your GCS bucket (for example, us-central1)

Runtime Select Python 3.12 or later
In the Trigger (optional) section:
1. Click + Add trigger.
2. Select Cloud Pub/Sub.
3. In Select a Cloud Pub/Sub topic, choose the topic (cloudlock-data-export-trigger).
4. Click Save.
In the Authentication section:
1. Select Require authentication.
2. Check Identity and Access Management (IAM).
Note: Pub/Sub will automatically handle authentication when invoking the function.
Scroll down and expand Containers, Networking, Security.
Go to the Security tab:
- Service account: Select the service account (cloudlock-data-export-sa).

Setting	Value
Service name	`cloudlock-data-export`
Region	Select region matching your GCS bucket (for example, `us-central1`)
Runtime	Select Python 3.12 or later

Go to the Containers tab:

Click Variables & Secrets.
Click + Add variable for each environment variable:

Variable Name	Example Value	Description
`GCS_BUCKET`	`cisco-cloudlock-logs`	GCS bucket name
`GCS_PREFIX`	`cloudlock/`	Prefix for log files
`STATE_KEY`	`cloudlock/state.json`	State file path
`CLOUDLOCK_API_TOKEN`	`your-api-token`	API Bearer token
`CLOUDLOCK_API_BASE`	`https://your-cloudlock-api-server`	API base URL from CloudLock Support

Scroll down in the Variables & Secrets tab to Requests:
- Request timeout: Enter 600 seconds (10 minutes).
Go to the Settings tab in Containers:
- In the Resources section:
  - Memory: Select 512 MiB or higher.
  - CPU: Select 1.
- Click Done.
Scroll down to Execution environment:
- Select Default (recommended).
In the Revision scaling section:
- Minimum number of instances: Enter 0.
- Maximum number of instances: Enter 100 (or adjust based on expected load).
Click Create.
Wait for the service to be created (1-2 minutes).
After the service is created, the inline code editor will open automatically.

Add function code

Enter main in the Entry point field.

In the inline code editor, create two files:

main.py:

import functions_framework
from google.cloud import storage
import json
import os
import urllib3
from datetime import datetime, timezone, timedelta
import time

# Initialize HTTP client with timeouts
http = urllib3.PoolManager(
    timeout=urllib3.Timeout(connect=5.0, read=30.0),
    retries=False,
)

# Initialize Storage client
storage_client = storage.Client()

@functions_framework.cloud_event
def main(cloud_event):
    """
    Cloud Run function triggered by Pub/Sub to fetch logs from Cisco CloudLock API and write to GCS.

    Args:
        cloud_event: CloudEvent object containing Pub/Sub message
    """

    # Get environment variables
    bucket_name = os.environ.get('GCS_BUCKET')
    prefix = os.environ.get('GCS_PREFIX', 'cloudlock/')
    state_key = os.environ.get('STATE_KEY', 'cloudlock/state.json')
    api_token = os.environ.get('CLOUDLOCK_API_TOKEN')
    api_base = os.environ.get('CLOUDLOCK_API_BASE')

    if not all([bucket_name, api_token, api_base]):
        print('Error: Missing required environment variables')
        return

    try:
        # Get GCS bucket
        bucket = storage_client.bucket(bucket_name)

        # Load state (last processed offset for each endpoint)
        state = load_state(bucket, state_key)

        print(f'Processing logs with state: {state}')

        # Create Authorization header
        headers = {
            'Authorization': f'Bearer {api_token}',
            'Content-Type': 'application/json'
        }

        # Fetch incidents data (using offset-based pagination)
        incidents_offset = state.get('incidents_offset', 0)
        incidents, new_incidents_offset = fetch_cloudlock_incidents(
            http, api_base, headers, incidents_offset
        )

        if incidents:
            upload_to_gcs_ndjson(bucket, prefix, 'incidents', incidents)
            print(f'Uploaded {len(incidents)} incidents to GCS')
            state['incidents_offset'] = new_incidents_offset

        # Fetch activities data (using time-based filtering with offset pagination)
        activities_last_time = state.get('activities_last_time')
        if not activities_last_time:
            activities_last_time = (datetime.now(timezone.utc) - timedelta(hours=24)).isoformat()

        activities_offset = state.get('activities_offset', 0)
        activities, new_activities_offset, newest_activity_time = fetch_cloudlock_activities(
            http, api_base, headers, activities_last_time, activities_offset
        )

        if activities:
            upload_to_gcs_ndjson(bucket, prefix, 'activities', activities)
            print(f'Uploaded {len(activities)} activities to GCS')
            state['activities_offset'] = new_activities_offset
            if newest_activity_time:
                state['activities_last_time'] = newest_activity_time

        # Fetch entities data (using offset-based pagination)
        entities_offset = state.get('entities_offset', 0)
        entities, new_entities_offset = fetch_cloudlock_entities(
            http, api_base, headers, entities_offset
        )

        if entities:
            upload_to_gcs_ndjson(bucket, prefix, 'entities', entities)
            print(f'Uploaded {len(entities)} entities to GCS')
            state['entities_offset'] = new_entities_offset

        # Update consolidated state
        state['updated_at'] = datetime.now(timezone.utc).isoformat()
        save_state(bucket, state_key, state)

        print('CloudLock data export completed successfully')

    except Exception as e:
        print(f'Error processing logs: {str(e)}')
        raise

def make_api_request(http, url, headers, retries=3):
    """Make API request with exponential backoff retry logic."""
    backoff = 1.0
    for attempt in range(retries):
        try:
            response = http.request('GET', url, headers=headers)

            if response.status == 200:
                return response
            elif response.status == 429:
                # Rate limit - CloudLock enforces per-endpoint rate limits
                retry_after = int(response.headers.get('Retry-After', str(int(backoff))))
                print(f'Rate limited (429), waiting {retry_after} seconds')
                time.sleep(retry_after)
                backoff = min(backoff * 2, 60.0)
            else:
                print(f'API request failed with status {response.status}: {response.data.decode("utf-8")}')
                if response.status in (401, 403):
                    return None
        except Exception as e:
            print(f'Request attempt {attempt + 1} failed: {str(e)}')
            if attempt < retries - 1:
                wait_time = 2 ** attempt
                time.sleep(wait_time)
            else:
                raise

    return None

def fetch_cloudlock_incidents(http, api_base, headers, start_offset=0):
    """
    Fetch incidents data from Cisco CloudLock API using offset-based pagination.

    Endpoint: GET /api/v2/incidents
    Pagination: limit and offset query parameters.
    Response: JSON with 'items' array, 'total' count, and 'results' count.
    """
    url = f"{api_base}/api/v2/incidents"

    limit = 1000
    offset = start_offset
    all_data = []

    try:
        while True:
            # Build URL with parameters
            full_url = f"{url}?limit={limit}&offset={offset}"

            print(f"Fetching incidents with offset: {offset}")

            response = make_api_request(http, full_url, headers)
            if not response:
                break

            data = json.loads(response.data.decode('utf-8'))

            # CloudLock API returns items in 'items' array
            batch_data = data.get('items', [])

            if not batch_data:
                print("No more incidents to fetch")
                break

            all_data.extend(batch_data)

            # Check if we've reached the end
            total = data.get('total', 0)
            results = data.get('results', len(batch_data))

            print(f"Fetched {results} incidents (total available: {total})")

            if results < limit or offset + results >= total:
                print("Reached end of incidents")
                break

            offset += limit

        print(f"Fetched {len(all_data)} total incidents")
        return all_data, offset

    except Exception as e:
        print(f"Error fetching incidents: {str(e)}")
        return [], start_offset

def fetch_cloudlock_activities(http, api_base, headers, from_time, start_offset=0):
    """
    Fetch activities data from Cisco CloudLock API using time-based filtering and offset pagination.

    Endpoint: GET /api/v2/activities
    Pagination: limit and offset query parameters.
    Note: Client-side time filtering is applied since the API may not support time range parameters.
    """
    url = f"{api_base}/api/v2/activities"

    limit = 1000
    offset = start_offset
    all_data = []
    newest_time = None

    try:
        while True:
            # Build URL with pagination
            full_url = f"{url}?limit={limit}&offset={offset}"

            print(f"Fetching activities with offset: {offset}")

            response = make_api_request(http, full_url, headers)
            if not response:
                break

            data = json.loads(response.data.decode('utf-8'))
            batch_data = data.get('items', [])

            if not batch_data:
                print("No more activities to fetch")
                break

            # Filter activities by time (client-side filtering)
            filtered_batch = []
            for item in batch_data:
                item_time = item.get('timestamp') or item.get('created_at')
                if item_time and item_time >= from_time:
                    filtered_batch.append(item)
                    if not newest_time or item_time > newest_time:
                        newest_time = item_time

            all_data.extend(filtered_batch)

            results = data.get('results', len(batch_data))
            total = data.get('total', 0)

            print(f"Fetched {results} activities, {len(filtered_batch)} after time filter (total available: {total})")

            if results < limit or offset + results >= total:
                print("Reached end of activities")
                break

            offset += limit

        print(f"Fetched {len(all_data)} total activities")
        return all_data, offset, newest_time

    except Exception as e:
        print(f"Error fetching activities: {str(e)}")
        return [], start_offset, None

def fetch_cloudlock_entities(http, api_base, headers, start_offset=0):
    """
    Fetch entities data from Cisco CloudLock API using offset-based pagination.

    Endpoint: GET /api/v2/entities
    Note: This endpoint requires the Entity Cache feature. If not enabled,
    use the incident entities endpoint (/api/v2/incidents/{id}/entities) as an alternative.
    """
    url = f"{api_base}/api/v2/entities"

    limit = 1000
    offset = start_offset
    all_data = []

    try:
        while True:
            full_url = f"{url}?limit={limit}&offset={offset}"

            print(f"Fetching entities with offset: {offset}")

            response = make_api_request(http, full_url, headers)
            if not response:
                break

            data = json.loads(response.data.decode('utf-8'))
            batch_data = data.get('items', [])

            if not batch_data:
                print("No more entities to fetch")
                break

            all_data.extend(batch_data)

            results = data.get('results', len(batch_data))
            total = data.get('total', 0)

            print(f"Fetched {results} entities (total available: {total})")

            if results < limit or offset + results >= total:
                print("Reached end of entities")
                break

            offset += limit

        print(f"Fetched {len(all_data)} total entities")
        return all_data, offset

    except Exception as e:
        print(f"Error fetching entities: {str(e)}")
        return [], start_offset

def upload_to_gcs_ndjson(bucket, prefix, data_type, data):
    """Upload data to GCS bucket in NDJSON format (one JSON object per line)."""
    timestamp = datetime.now(timezone.utc).strftime('%Y/%m/%d/%H')
    filename = f"{prefix}{data_type}/{timestamp}/cloudlock_{data_type}_{int(datetime.now(timezone.utc).timestamp())}.jsonl"

    try:
        # Convert to NDJSON format
        ndjson_content = '\n'.join([json.dumps(item, separators=(',', ':')) for item in data])

        blob = bucket.blob(filename)
        blob.upload_from_string(
            ndjson_content,
            content_type='application/x-ndjson'
        )

        print(f"Successfully uploaded {filename} to GCS")

    except Exception as e:
        print(f"Error uploading to GCS: {str(e)}")
        raise

def load_state(bucket, key):
    """Load state from GCS with separate tracking for each endpoint."""
    try:
        blob = bucket.blob(key)
        if blob.exists():
            state_data = blob.download_as_text()
            return json.loads(state_data)
    except Exception as e:
        print(f'Warning: Could not load state: {str(e)}')

    print("No previous state found, starting fresh")
    return {}

def save_state(bucket, key, state):
    """Save consolidated state to GCS."""
    try:
        blob = bucket.blob(key)
        blob.upload_from_string(
            json.dumps(state, indent=2),
            content_type='application/json'
        )
        print("Updated state successfully")
    except Exception as e:
        print(f"Error updating state: {str(e)}")
        raise

requirements.txt:

functions-framework==3.*
google-cloud-storage==2.*
urllib3>=2.0.0

Click Deploy to save and deploy the function.
Wait for deployment to complete (2-3 minutes).

Note: This script uses Cisco CloudLock API v2 endpoints (/api/v2/incidents, /api/v2/activities, /api/v2/entities) with offset-based pagination (limit and offset query parameters) as documented in the Cisco DevNet CloudLock API reference. The Entities endpoint requires the Entity Cache feature to be enabled. For production deployments, contact Cisco CloudLock Support to verify the most current API capabilities for your tenant.

Create Cloud Scheduler job

Cloud Scheduler will publish messages to the Pub/Sub topic at regular intervals, triggering the Cloud Run function.

In the GCP Console, go to Cloud Scheduler.
Click Create Job.

Provide the following configuration details:

Setting	Value
Name	`cloudlock-data-export-hourly`
Region	Select same region as Cloud Run function
Frequency	`0 * * * *` (every hour, on the hour)
Timezone	Select timezone (UTC recommended)
Target type	Pub/Sub
Topic	Select the topic (`cloudlock-data-export-trigger`)
Message body	`{}` (empty JSON object)

Click Create.

Schedule frequency options

Choose frequency based on log volume and latency requirements:

Frequency	Cron Expression	Use Case
Every 5 minutes	`/5 * * *`	High-volume, low-latency
Every 15 minutes	`/15 * * *`	Medium volume
Every hour	`0 * * * *`	Standard (recommended)
Every 6 hours	`0 /6 * *`	Low volume, batch processing
Daily	`0 0 * * *`	Historical data collection

Test the integration

In the Cloud Scheduler console, find your job.
Click Force run to trigger the job manually.
Wait a few seconds.
Go to Cloud Run > Services.
Click on the function name (cloudlock-data-export).
Click the Logs tab.

Verify the function executed successfully. Look for:

Processing logs with state: {}
Fetching incidents with offset: 0
Fetched X incidents (total available: Y)
Uploaded X incidents to GCS
CloudLock data export completed successfully

Go to Cloud Storage > Buckets.
Click on the bucket name (cisco-cloudlock-logs).
Navigate to the cloudlock/ prefix folder.
Verify that new .jsonl files were created under the incidents/, activities/, or entities/ subfolders.

If you see errors in the logs:

HTTP 401: Check the API token in environment variables
HTTP 403: Verify the CloudLock account has the required permissions
HTTP 429: Rate limiting - the function will automatically retry with backoff
Missing environment variables: Check all required variables are set
Empty entities: The Entities endpoint requires the Entity Cache feature - contact CloudLock Support to enable it

Configure a feed in Google SecOps to ingest Cisco CloudLock logs

Go to SIEM Settings > Feeds.
Click Add New Feed.
Click Configure a single feed.
In the Feed name field, enter a name for the feed (for example, Cisco CloudLock logs).
Select Google Cloud Storage V2 as the Source type.
Select Cisco CloudLock as the Log type.
Click Get Service Account. A unique service account email will be displayed, for example:
```
chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com
```
Copy this email address for use in the next step.

Note: Each Google SecOps instance has a unique service account. Do not use service accounts from other documentation or examples.
Click Next.
Specify values for the following input parameters:
- Storage bucket URL: Enter the GCS bucket URI with the prefix path:
```
gs://cisco-cloudlock-logs/cloudlock/
```
  - Replace:
    - cisco-cloudlock-logs: Your GCS bucket name.
    - cloudlock/: Optional prefix/folder path where logs are stored (leave empty for root).
Note: Always include the trailing slash (/) at the end of the URI.
- Source deletion option: Select the deletion option according to your preference:
  - Never: Never deletes any files after transfers (recommended for testing).
  - Delete transferred files: Deletes files after successful transfer.
  - Delete transferred files and empty directories: Deletes files and empty directories after successful transfer.
  Note: If you select a deletion option, the service account must have Storage Object Admin role instead of Storage Object Viewer. Update IAM permissions accordingly.
- Maximum File Age: Include files modified in the last number of days (default is 180 days).
- Asset namespace: The asset namespace.
- Ingestion labels: The label to be applied to the events from this feed.
Click Next.
Review your new feed configuration in the Finalize screen, and then click Submit.

Grant IAM permissions to the Google SecOps service account

The Google SecOps service account needs Storage Object Viewer role on your GCS bucket.

Go to Cloud Storage > Buckets.
Click your bucket name.
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Paste the Google SecOps service account email.
- Assign roles: Select Storage Object Viewer.
Click Save.

Note: If you plan to use the deletion option "Delete transferred files" or "Delete transferred files and empty directories," grant Storage Object Admin role instead of Storage Object Viewer.

UDM mapping table

Log Field	UDM Mapping	Logic
created_at	about.resource.attribute.creation_time	The value of `created_at` from the raw log is parsed as a timestamp and mapped to the `about.resource.attribute.creation_time` field.
created_at	about.resource.attribute.labels.key	The value of `created_at` is assigned to the labels key.
created_at	about.resource.attribute.labels.value	The value of `created_at` is assigned to the labels value.
entity.id	target.asset.product_object_id	The value of `entity.id` from the raw log is mapped to the `target.asset.product_object_id` field.
entity.ip	target.ip	The value of `entity.ip` from the raw log is merged into the `target.ip` field.
entity.mime_type	target.file.mime_type	The value of `entity.mime_type` from the raw log is mapped to the `target.file.mime_type` field when `entity.origin_type` is "document".
entity.name	target.application	The value of `entity.name` from the raw log is mapped to the `target.application` field when `entity.origin_type` is "app".
entity.name	target.file.full_path	The value of `entity.name` from the raw log is mapped to the `target.file.full_path` field when `entity.origin_type` is "document".
entity.origin_id	target.resource.product_object_id	The value of `entity.origin_id` from the raw log is mapped to the `target.resource.product_object_id` field.
entity.origin_type	target.resource.resource_subtype	The value of `entity.origin_type` from the raw log is mapped to the `target.resource.resource_subtype` field.
entity.owner_email	target.user.email_addresses	The value of `entity.owner_email` from the raw log is merged into the `target.user.email_addresses` field if it matches an email regex.
entity.owner_email	target.user.user_display_name	The value of `entity.owner_email` from the raw log is mapped to the `target.user.user_display_name` field if it does not match an email regex.
entity.owner_name	target.user.user_display_name	The value of `entity.owner_name` from the raw log is mapped to the `target.user.user_display_name` field when `entity.owner_email` matches an email regex.
entity.vendor.name	target.platform_version	The value of `entity.vendor.name` from the raw log is mapped to the `target.platform_version` field.
id	metadata.product_log_id	The value of `id` from the raw log is mapped to the `metadata.product_log_id` field.
incident_status	metadata.product_event_type	The value of `incident_status` from the raw log is mapped to the `metadata.product_event_type` field.
policy.id	security_result.rule_id	The value of `policy.id` from the raw log is mapped to the `security_result.rule_id` field.
policy.name	security_result.rule_name	The value of `policy.name` from the raw log is mapped to the `security_result.rule_name` field.
severity	security_result.severity_details	The value of `severity` from the raw log is mapped to the `security_result.severity_details` field.
updated_at	about.resource.attribute.labels.key	The value of `updated_at` is assigned to the labels key.
updated_at	about.resource.attribute.labels.value	The value of `updated_at` is assigned to the labels value.
updated_at	about.resource.attribute.last_update_time	The value of `updated_at` from the raw log is parsed as a timestamp and mapped to the `about.resource.attribute.last_update_time` field.
N/A	metadata.event_timestamp	Derived from the `updated_at` field. The value is parsed as a timestamp and mapped.
N/A	metadata.event_type	Set to "GENERIC_EVENT".
N/A	metadata.product_name	Set to "CISCO_CLOUDLOCK_CASB".
N/A	metadata.product_version	Set to "Cisco".
N/A	metadata.vendor_name	Set to "CloudLock".
N/A	security_result.alert_state	Set to "ALERTING" if `severity` is "ALERT" and `incident_status` is not "RESOLVED" or "DISMISSED". Set to "NOT_ALERTING" if `severity` is "ALERT" and `incident_status` is "RESOLVED" or "DISMISSED".
N/A	security_result.detection_fields.key	Derived from the `matches` array, specifically the key of each match object. Also set to "true" if `severity` is "ALERT" and `incident_status` is "NEW".
N/A	security_result.detection_fields.value	Derived from the `matches` array, specifically the value of each match object. Also set to "true" if `severity` is "ALERT" and `incident_status` is "NEW".
N/A	security_result.severity	Set to "INFORMATIONAL" if `severity` is "INFO". Set to "CRITICAL" if `severity` is "CRITICAL". Derived from the `severity` field.
N/A	security_result.summary	Set to "match count: " concatenated with the value of `match_count`.
N/A	target.resource.resource_type	Set to "STORAGE_OBJECT" when `entity.origin_type` is "document".
N/A	target.url	Derived from `entity.direct_url` when `entity.origin_type` is "document".

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