Collect Censys logs

Supported in:

Google secops SIEM

Note: This feature is covered by Pre-GA Offerings Terms of the Google Security Operations Service Specific Terms. Pre-GA features might have limited support, and changes to pre-GA features might not be compatible with other pre-GA versions. For more information, see the Google SecOps Technical Support Service guidelines and the Google SecOps Service Specific Terms.

This document explains how to ingest Censys logs to Google Security Operations using Google Cloud Storage V2.

Censys provides comprehensive attack surface management and internet intelligence through its API. This integration lets you collect host discovery events, risk events, and asset changes from Censys ASM and forward them to Google SecOps for analysis and monitoring.

Before you begin

Ensure 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 Censys ASM

Collect Censys API credentials

1. Sign in to the Censys ASM Console at app.censys.io.
2. Go to Integrations at the top of the page.
3. Copy and save your API Key and Organization ID.
4. Note the API Base URL: https://api.platform.censys.io

Create Google Cloud Storage bucket

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

5. Provide the following configuration details:

   Setting	Value
   Name your bucket	Enter a globally unique name (for example, censys-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

6. Click Create.

Create service account for Cloud Run function

The Cloud Run function needs a service account with permissions to write to GCS bucket.

Create service account

1. In the GCP Console, go to IAM & Admin > Service Accounts.
2. Click Create Service Account.
3. Provide the following configuration details:
   • Service account name: Enter censys-data-collector-sa.
   • Service account description: Enter Service account for Cloud Run function to collect Censys logs.
4. Click Create and Continue.
5. In the Grant this service account access to project section, add the following roles:
   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.
6. Click Continue.
7. 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:

1. Go to Cloud Storage > Buckets.
2. Click on your bucket name.
3. Go to the Permissions tab.
4. Click Grant access.
5. Provide the following configuration details:
   • Add principals: Enter the service account email (for example, censys-data-collector-sa@PROJECT_ID.iam.gserviceaccount.com).
   • Assign roles: Select Storage Object Admin.
6. 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.

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

Create Cloud Run function to collect logs

The Cloud Run function is triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from Censys ASM API and writes them to GCS.

1. In the GCP Console, go to Cloud Run.
2. Click Create service.
3. Select Function (use an inline editor to create a function).

4. In the Configure section, provide the following configuration details:

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

5. In the Trigger (optional) section:

   1. Click + Add trigger.
   2. Select Cloud Pub/Sub.
   3. In Select a Cloud Pub/Sub topic, choose the Pub/Sub topic (censys-data-trigger).
   4. Click Save.

6. In the Authentication section:

   • Select Require authentication.
   • Check Identity and Access Management (IAM).

Note: Pub/Sub automatically handles authentication when invoking the function.

1. Scroll down and expand Containers, Networking, Security.
2. Go to the Security tab:
   • Service account: Select the service account (censys-data-collector-sa).

3. Go to the Containers tab:

   1. Click Variables & Secrets.

   2. Click + Add variable for each environment variable:

      Variable Name	Example Value
      GCS_BUCKET	censys-logs
      GCS_PREFIX	censys/
      STATE_KEY	censys/state.json
      CENSYS_API_KEY	your-censys-api-key
      CENSYS_ORG_ID	your-organization-id
      API_BASE	https://api.platform.censys.io

4. Scroll down in the Variables & Secrets tab to Requests:

   • Request timeout: Enter 600 seconds (10 minutes).

5. Go to the Settings tab in Containers:

   • In the Resources section:
     • Memory: Select 512 MiB or higher.
     • CPU: Select 1.
   • Click Done.

6. Scroll to Execution environment:

   • Select Default (recommended).

7. In the Revision scaling section:

   • Minimum number of instances: Enter 0.
   • Maximum number of instances: Enter 100 (or adjust based on expected load).

8. Click Create.

9. Wait for the service to be created (1-2 minutes).

10. After the service is created, the inline code editor opens automatically.

Add function code

1. Enter main in Function entry point

2. In the inline code editor, create two files:

   • First file: main.py:

   import functions_framework
   from google.cloud import storage
   import json
   import urllib3
   import gzip
   import os
   from datetime import datetime, timedelta, timezone
   from typing import Dict, List, Any, Optional
   from urllib.parse import urlencode
   
   # 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 Censys ASM 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', 'censys/')
       state_key = os.environ.get('STATE_KEY', 'censys/state.json')
       censys_api_key = os.environ.get('CENSYS_API_KEY')
       censys_org_id = os.environ.get('CENSYS_ORG_ID')
       api_base = os.environ.get('API_BASE', 'https://api.platform.censys.io')
   
       if not all([bucket_name, censys_api_key, censys_org_id]):
           print('Error: Missing required environment variables')
           return
   
       try:
           collector = CensysCollector(
               bucket_name=bucket_name,
               prefix=prefix,
               state_key=state_key,
               api_key=censys_api_key,
               org_id=censys_org_id,
               api_base=api_base
           )
   
           # Get last collection time
           last_collection_time = collector.get_last_collection_time()
           current_time = datetime.now(timezone.utc)
   
           print(f'Collecting events since {last_collection_time}')
   
           # Collect different types of events
           logbook_events = collector.collect_logbook_events()
           risk_events = collector.collect_risks_events()
   
           # Save events to GCS
           collector.save_events_to_gcs(logbook_events, 'logbook')
           collector.save_events_to_gcs(risk_events, 'risks')
   
           # Update state
           collector.save_collection_time(current_time)
   
           print(f'Successfully processed {len(logbook_events)} logbook events and {len(risk_events)} risk events')
   
       except Exception as e:
           print(f'Error processing logs: {str(e)}')
           raise
   
   class CensysCollector:
       def __init__(self, bucket_name: str, prefix: str, state_key: str, 
                    api_key: str, org_id: str, api_base: str):
           self.bucket_name = bucket_name
           self.prefix = prefix
           self.state_key = state_key
           self.headers = {
               'Authorization': f'Bearer {api_key}',
               'X-Organization-ID': org_id,
               'Content-Type': 'application/json'
           }
           self.api_base = api_base
           self.bucket = storage_client.bucket(bucket_name)
   
       def get_last_collection_time(self) -> Optional[datetime]:
           """Get the last collection timestamp from GCS state file."""
           try:
               blob = self.bucket.blob(self.state_key)
               if blob.exists():
                   state_data = blob.download_as_text()
                   state = json.loads(state_data)
                   return datetime.fromisoformat(state.get('last_collection_time', '2024-01-01T00:00:00Z'))
           except Exception as e:
               print(f'No state file found or error reading state: {e}')
           return datetime.now(timezone.utc) - timedelta(hours=1)
   
       def save_collection_time(self, collection_time: datetime):
           """Save the current collection timestamp to GCS state file."""
           state = {'last_collection_time': collection_time.strftime('%Y-%m-%dT%H:%M:%SZ')}
           blob = self.bucket.blob(self.state_key)
           blob.upload_from_string(
               json.dumps(state),
               content_type='application/json'
           )
   
       def collect_logbook_events(self, cursor: str = None) -> List[Dict[str, Any]]:
           """Collect logbook events from Censys ASM API using cursor-based pagination."""
           events = []
           url = f"{self.api_base}/v3/logbook"
   
           params = {}
           if cursor:
               params['cursor'] = cursor
   
           try:
               query_string = urlencode(params) if params else ''
               full_url = f"{url}?{query_string}" if query_string else url
   
               response = http.request('GET', full_url, headers=self.headers)
   
               # Handle rate limiting with exponential backoff
               if response.status == 429:
                   retry_after = int(response.headers.get('Retry-After', '60'))
                   print(f'Rate limited (429). Retrying after {retry_after}s...')
                   import time
                   time.sleep(retry_after)
                   return self.collect_logbook_events(cursor)
   
               if response.status != 200:
                   print(f'API request failed with status {response.status}: {response.data}')
                   return []
   
               data = json.loads(response.data.decode('utf-8'))
               events.extend(data.get('logbook_entries', []))
   
               # Handle cursor-based pagination
               next_cursor = data.get('next_cursor')
               if next_cursor:
                   events.extend(self.collect_logbook_events(next_cursor))
   
               print(f'Collected {len(events)} logbook events')
               return events
   
           except Exception as e:
               print(f'Error collecting logbook events: {e}')
               return []
   
       def collect_risks_events(self) -> List[Dict[str, Any]]:
           """Collect risk events from Censys ASM API."""
           events = []
           url = f"{self.api_base}/v3/risks"
   
           try:
               response = http.request('GET', url, headers=self.headers)
   
               # Handle rate limiting with exponential backoff
               if response.status == 429:
                   retry_after = int(response.headers.get('Retry-After', '60'))
                   print(f'Rate limited (429). Retrying after {retry_after}s...')
                   import time
                   time.sleep(retry_after)
                   return self.collect_risks_events()
   
               if response.status != 200:
                   print(f'API request failed with status {response.status}: {response.data}')
                   return []
   
               data = json.loads(response.data.decode('utf-8'))
               events.extend(data.get('risks', []))
   
               print(f'Collected {len(events)} risk events')
               return events
   
           except Exception as e:
               print(f'Error collecting risk events: {e}')
               return []
   
       def save_events_to_gcs(self, events: List[Dict[str, Any]], event_type: str):
           """Save events to GCS in compressed NDJSON format."""
           if not events:
               return
   
           timestamp = datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')
           filename = f"{self.prefix}{event_type}_{timestamp}.json.gz"
   
           try:
               # Convert events to newline-delimited JSON
               ndjson_content = '\n'.join(json.dumps(event, separators=(',', ':')) for event in events)
   
               # Compress with gzip
               gz_bytes = gzip.compress(ndjson_content.encode('utf-8'))
   
               blob = self.bucket.blob(filename)
               blob.upload_from_string(
                   gz_bytes,
                   content_type='application/gzip'
               )
   
               print(f'Saved {len(events)} {event_type} events to {filename}')
   
           except Exception as e:
               print(f'Error saving {event_type} events to GCS: {e}')
               raise
   

   • Second file: requirements.txt:

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

3. Click Deploy to save and deploy the function.

4. Wait for deployment to complete (2-3 minutes).

Note: The Pub/Sub trigger configuration automatically creates the necessary subscriptions and permissions.

Create Cloud Scheduler job

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

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

3. Provide the following configuration details:

   Setting	Value
   Name	censys-data-collector-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 Pub/Sub topic (censys-data-trigger)
   Message body	{} (empty JSON object)

4. 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

1. In the Cloud Scheduler console, find your job.
2. Click Force run to trigger the job manually.
3. Wait a few seconds.
4. Go to Cloud Run > Services.
5. Click on your function name (censys-data-collector).
6. Click the Logs tab.

7. Verify the function executed successfully. Look for the following:

   Collecting events since YYYY-MM-DDTHH:MM:SS+00:00
   Collected X logbook events
   Collected X risk events
   Saved X logbook events to censys/logbook_YYYYMMDD_HHMMSS.json.gz
   Saved X risks events to censys/risks_YYYYMMDD_HHMMSS.json.gz
   Successfully processed X logbook events and X risk events
   

8. Go to Cloud Storage > Buckets.

9. Click on your bucket name.

10. Navigate to the prefix folder (censys/).

11. Verify that new .json.gz files were created with the current timestamp.

If you see errors in the logs:

• HTTP 401: Check API credentials in environment variables
• HTTP 403: Verify account has required permissions
• HTTP 429: Rate limiting - function will automatically retry with backoff
• Missing environment variables: Check all required variables are set

Retrieve the Google SecOps service account

Google SecOps uses a unique service account to read data from your GCS bucket. You must grant this service account access to your bucket.

Get the service account email

1. Go to SIEM Settings > Feeds.
2. Click Add New Feed.
3. Click Configure a single feed.
4. In the Feed name field, enter a name for the feed (for example, Censys logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select CENSYS as the Log type.
7. Click Get Service Account.

8. A unique service account email will be displayed, for example:

   [e-mail]
   

9. 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.

Grant IAM permissions to the Google SecOps service account

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

1. Go to Cloud Storage > Buckets.
2. Click on your bucket name.
3. Go to the Permissions tab.
4. Click Grant access.
5. Provide the following configuration details:
   • Add principals: Paste the Google SecOps service account email.
   • Assign roles: Select Storage Object Viewer.
6. 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.

Configure a feed in Google SecOps to ingest Censys logs

1. Go to SIEM Settings > Feeds.
2. Click Add New Feed.
3. Click Configure a single feed.
4. In the Feed name field, enter a name for the feed (for example, Censys logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select CENSYS as the Log type.
7. Click Next.

8. Specify values for the following input parameters:

   • Storage bucket URL: Enter the GCS bucket URI with the prefix path:

     gs://censys-logs/censys/
     

     • Replace:

       • censys-logs: Your GCS bucket name.
       • censys/: Optional prefix/folder path where logs are stored (leave empty for root).

     • Examples:

       • Root bucket: gs://censys-logs/
       • With prefix: gs://censys-logs/censys/
     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.

9. Click Next.

10. Review your new feed configuration in the Finalize screen, and then click Submit.

UDM mapping table

Log field	UDM mapping	Logic
assetId	read_only_udm.principal.asset.hostname	If the assetId field is not an IP address, it is mapped to principal.asset.hostname.
assetId	read_only_udm.principal.asset.ip	If the assetId field is an IP address, it is mapped to principal.asset.ip.
assetId	read_only_udm.principal.hostname	If the assetId field is not an IP address, it is mapped to principal.hostname.
assetId	read_only_udm.principal.ip	If the assetId field is an IP address, it is mapped to principal.ip.
associatedAt	read_only_udm.security_result.detection_fields.value	The associatedAt field is mapped to security_result.detection_fields.value.
autonomousSystem.asn	read_only_udm.additional.fields.value.string_value	The autonomousSystem.asn field is converted to a string and mapped to additional.fields.value.string_value with key "autonomousSystem_asn".
autonomousSystem.bgpPrefix	read_only_udm.additional.fields.value.string_value	The autonomousSystem.bgpPrefix field is mapped to additional.fields.value.string_value with key "autonomousSystem_bgpPrefix".
banner	read_only_udm.principal.resource.attribute.labels.value	The banner field is mapped to principal.resource.attribute.labels.value with key "banner".
cloud	read_only_udm.metadata.vendor_name	The cloud field is mapped to metadata.vendor_name.
comments.refUrl	read_only_udm.network.http.referral_url	The comments.refUrl field is mapped to network.http.referral_url.
data.cve	read_only_udm.additional.fields.value.string_value	The data.cve field is mapped to additional.fields.value.string_value with key "data_cve".
data.cvss	read_only_udm.additional.fields.value.string_value	The data.cvss field is mapped to additional.fields.value.string_value with key "data_cvss".
data.ipAddress	read_only_udm.principal.asset.ip	If the data.ipAddress field is not equal to the assetId field, it is mapped to principal.asset.ip.
data.ipAddress	read_only_udm.principal.ip	If the data.ipAddress field is not equal to the assetId field, it is mapped to principal.ip.
data.location.city	read_only_udm.principal.location.city	If the location.city field is empty, the data.location.city field is mapped to principal.location.city.
data.location.countryCode	read_only_udm.principal.location.country_or_region	If the location.country field is empty, the data.location.countryCode field is mapped to principal.location.country_or_region.
data.location.latitude	read_only_udm.principal.location.region_coordinates.latitude	If the location.coordinates.latitude and location.geoCoordinates.latitude fields are empty, the data.location.latitude field is converted to a float and mapped to principal.location.region_coordinates.latitude.
data.location.longitude	read_only_udm.principal.location.region_coordinates.longitude	If the location.coordinates.longitude and location.geoCoordinates.longitude fields are empty, the data.location.longitude field is converted to a float and mapped to principal.location.region_coordinates.longitude.
data.location.province	read_only_udm.principal.location.state	If the location.province field is empty, the data.location.province field is mapped to principal.location.state.
data.mailServers	read_only_udm.additional.fields.value.list_value.values.string_value	Each element in the data.mailServers array is mapped to a separate additional.fields entry with key "Mail Servers" and value.list_value.values.string_value set to the element value.
data.names.forwardDns[].name	read_only_udm.network.dns.questions.name	Each element in the data.names.forwardDns array is mapped to a separate network.dns.questions entry with the name field set to the element's name field.
data.nameServers	read_only_udm.additional.fields.value.list_value.values.string_value	Each element in the data.nameServers array is mapped to a separate additional.fields entry with key "Name nameServers" and value.list_value.values.string_value set to the element value.
data.protocols[].transportProtocol	read_only_udm.network.ip_protocol	If the data.protocols[].transportProtocol field is one of TCP, EIGRP, ESP, ETHERIP, GRE, ICMP, IGMP, IP6IN4, PIM, UDP, or VRRP, it is mapped to network.ip_protocol.
data.protocols[].transportProtocol	read_only_udm.principal.resource.attribute.labels.value	The data.protocols[].transportProtocol field is mapped to principal.resource.attribute.labels.value with key "data_protocols {index}".
http.request.headers[].key, http.request.headers[].value.headers.0	read_only_udm.network.http.user_agent	If the http.request.headers[].key field is "User-Agent", the corresponding http.request.headers[].value.headers.0 field is mapped to network.http.user_agent.
http.request.headers[].key, http.request.headers[].value.headers.0	read_only_udm.network.http.parsed_user_agent	If the http.request.headers[].key field is "User-Agent", the corresponding http.request.headers[].value.headers.0 field is parsed as a user agent string and mapped to network.http.parsed_user_agent.
http.request.headers[].key, http.request.headers[].value.headers.0	read_only_udm.principal.resource.attribute.labels.key, read_only_udm.principal.resource.attribute.labels.value	For each element in the http.request.headers array, the key field is mapped to principal.resource.attribute.labels.key and value.headers.0 field is mapped to principal.resource.attribute.labels.value.
http.request.uri	read_only_udm.principal.asset.hostname	The hostname part of the http.request.uri field is extracted and mapped to principal.asset.hostname.
http.request.uri	read_only_udm.principal.hostname	The hostname part of the http.request.uri field is extracted and mapped to principal.hostname.
http.response.body	read_only_udm.principal.resource.attribute.labels.value	The http.response.body field is mapped to principal.resource.attribute.labels.value with key "http_response_body".
http.response.headers[].key, http.response.headers[].value.headers.0	read_only_udm.target.hostname	If the http.response.headers[].key field is "Server", the corresponding http.response.headers[].value.headers.0 field is mapped to target.hostname.
http.response.headers[].key, http.response.headers[].value.headers.0	read_only_udm.principal.resource.attribute.labels.key, read_only_udm.principal.resource.attribute.labels.value	For each element in the http.response.headers array, the key field is mapped to principal.resource.attribute.labels.key and value.headers.0 field is mapped to principal.resource.attribute.labels.value.
http.response.statusCode	read_only_udm.network.http.response_code	The http.response.statusCode field is converted to an integer and mapped to network.http.response_code.
ip	read_only_udm.target.asset.ip	The ip field is mapped to target.asset.ip.
ip	read_only_udm.target.ip	The ip field is mapped to target.ip.
isSeed	read_only_udm.additional.fields.value.string_value	The isSeed field is converted to a string and mapped to additional.fields.value.string_value with key "isSeed".
location.city	read_only_udm.principal.location.city	The location.city field is mapped to principal.location.city.
location.continent	read_only_udm.additional.fields.value.string_value	The location.continent field is mapped to additional.fields.value.string_value with key "location_continent".
location.coordinates.latitude	read_only_udm.principal.location.region_coordinates.latitude	The location.coordinates.latitude field is converted to a float and mapped to principal.location.region_coordinates.latitude.
location.coordinates.longitude	read_only_udm.principal.location.region_coordinates.longitude	The location.coordinates.longitude field is converted to a float and mapped to principal.location.region_coordinates.longitude.
location.country	read_only_udm.principal.location.country_or_region	The location.country field is mapped to principal.location.country_or_region.
location.geoCoordinates.latitude	read_only_udm.principal.location.region_coordinates.latitude	If the location.coordinates.latitude field is empty, the location.geoCoordinates.latitude field is converted to a float and mapped to principal.location.region_coordinates.latitude.
location.geoCoordinates.longitude	read_only_udm.principal.location.region_coordinates.longitude	If the location.coordinates.longitude field is empty, the location.geoCoordinates.longitude field is converted to a float and mapped to principal.location.region_coordinates.longitude.
location.postalCode	read_only_udm.additional.fields.value.string_value	The location.postalCode field is mapped to additional.fields.value.string_value with key "Postal code".
location.province	read_only_udm.principal.location.state	The location.province field is mapped to principal.location.state.
operation	read_only_udm.security_result.action_details	The operation field is mapped to security_result.action_details.
perspectiveId	read_only_udm.principal.group.product_object_id	The perspectiveId field is mapped to principal.group.product_object_id.
port	read_only_udm.principal.port	The port field is converted to an integer and mapped to principal.port.
risks[].severity, risks[].title	read_only_udm.security_result.category_details	The risks[].severity field is concatenated with the risks[].title field and mapped to security_result.category_details.
serviceName	read_only_udm.network.application_protocol	If the serviceName field is "HTTP" or "HTTPS", it is mapped to network.application_protocol.
sourceIp	read_only_udm.principal.asset.ip	The sourceIp field is mapped to principal.asset.ip.
sourceIp	read_only_udm.principal.ip	The sourceIp field is mapped to principal.ip.
timestamp	read_only_udm.metadata.event_timestamp	The timestamp field is parsed as a timestamp and mapped to metadata.event_timestamp.
transportFingerprint.id	read_only_udm.metadata.product_log_id	The transportFingerprint.id field is converted to a string and mapped to metadata.product_log_id.
transportFingerprint.raw	read_only_udm.additional.fields.value.string_value	The transportFingerprint.raw field is mapped to additional.fields.value.string_value with key "transportFingerprint_raw".
type	read_only_udm.metadata.product_event_type	The type field is mapped to metadata.product_event_type.
-	read_only_udm.metadata.product_name	The value "CENSYS_ASM" is assigned to metadata.product_name.
-	read_only_udm.metadata.vendor_name	The value "CENSYS" is assigned to metadata.vendor_name.
-	read_only_udm.metadata.event_type	The event type is determined based on the presence of specific fields: NETWORK_CONNECTION if has_princ_machine_id and has_target_machine are true and has_network_flow is false, NETWORK_DNS if has_network_flow is true, STATUS_UPDATE if has_princ_machine_id is true, and GENERIC_EVENT otherwise.

Need more help? Get answers from Community members and Google SecOps professionals.