Collect Entrust Identity Verification (formerly Onfido) logs

Supported in:

Google secops SIEM

This document explains how to ingest Entrust Identity Verification (formerly known as Onfido) logs to Google Security Operations using Google Cloud Storage V2. The parser transforms raw Onfido verification check and report logs into the Google SecOps UDM schema.

Entrust Identity Verification is a cloud-based identity verification platform that automates document verification, biometric analysis, and fraud detection. It provides a REST API for managing applicants, verification checks, and reports, enabling organizations to integrate identity verification workflows into their applications.

Before you begin

Make sure 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
• An Entrust Identity Verification (formerly Onfido) account with API access
• An Onfido API token with sufficient permissions to read checks and reports

Collect Onfido API credentials

Get API token

1. Sign in to the Onfido Dashboard.
2. Go to Developers > API Tokens.
3. Copy an existing live API token, or click Generate API token to create a new one.
4. Enter a name for the token (for example, Google Security Operations Integration).
5. Select Live as the token type.

6. Copy and save the API token securely.

   Note: The API token is displayed only once. Store it in a secure location immediately after creation.

Verify permissions

To verify the API token has the required permissions:

1. Sign in to the Onfido Dashboard.
2. Go to Developers > API Tokens.
3. Confirm the token is listed with Live status and is not revoked.
4. Verify the token has read access to checks and reports by testing API access.

Test API access

• Test your credentials before proceeding with the integration:

  # Replace with your actual API token
  ONFIDO_API_TOKEN="your-api-token"
  
  # Test API access - list checks
  curl -v -H "Authorization: Token token=${ONFIDO_API_TOKEN}" \
    "[https://api.onfido.com/v3.6/checks](https://api.onfido.com/v3.6/checks)"
  

A successful response returns HTTP 200 with a JSON object containing a checks array.

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, onfido-verification-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 and be invoked by Pub/Sub.

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 onfido-logs-collector-sa.
   • Service account description: Enter Service account for Cloud Run function to collect Onfido verification 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 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, onfido-logs-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 onfido-logs-trigger.
   • Leave other settings as default.
4. 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 verification checks and reports from the Onfido API and write 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	onfido-logs-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 onfido-logs-trigger.
   4. Click Save.

6. 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.

7. Scroll down and expand Containers, Networking, Security.

8. Go to the Security tab:

   • Service account: Select onfido-logs-collector-sa.

9. Go to the Containers tab:

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

   Variable Name	Example Value	Description
   GCS_BUCKET	onfido-verification-logs	GCS bucket name
   GCS_PREFIX	onfido-logs	Prefix for log files
   STATE_KEY	onfido-logs/state.json	State file path
   ONFIDO_API_TOKEN	your-api-token-here	Onfido API token
   MAX_RECORDS	1000	Max records per run
   PAGE_SIZE	100	Records per page
   LOOKBACK_HOURS	24	Initial lookback period

10. In the Variables & Secrets section, scroll down to Requests:

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

11. Go to the Settings tab:

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

12. In the Revision scaling section:

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

13. Click Create.

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

15. After the service is created, the inline code editor will open 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 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()
   
   # Environment variables
   GCS_BUCKET = os.environ.get('GCS_BUCKET')
   GCS_PREFIX = os.environ.get('GCS_PREFIX', 'onfido-logs')
   STATE_KEY = os.environ.get('STATE_KEY', 'onfido-logs/state.json')
   ONFIDO_API_TOKEN = os.environ.get('ONFIDO_API_TOKEN')
   MAX_RECORDS = int(os.environ.get('MAX_RECORDS', '1000'))
   PAGE_SIZE = int(os.environ.get('PAGE_SIZE', '100'))
   LOOKBACK_HOURS = int(os.environ.get('LOOKBACK_HOURS', '24'))
   
   API_BASE = '[https://api.onfido.com/v3.6](https://api.onfido.com/v3.6)'
   
   def parse_datetime(value: str) -> datetime:
       """Parse ISO datetime string to datetime object."""
       if value.endswith("Z"):
           value = value[:-1] + "+00:00"
       return datetime.fromisoformat(value)
   
   @functions_framework.cloud_event
   def main(cloud_event):
       """
       Cloud Run function triggered by Pub/Sub to fetch Onfido verification
       checks and reports and write to GCS.
   
       Args:
           cloud_event: CloudEvent object containing Pub/Sub message
       """
   
       if not all([GCS_BUCKET, ONFIDO_API_TOKEN]):
           print('Error: Missing required environment variables')
           return
   
       try:
           # Get GCS bucket
           bucket = storage_client.bucket(GCS_BUCKET)
   
           # Load state
           state = load_state(bucket, STATE_KEY)
   
           # Determine time window
           now = datetime.now(timezone.utc)
           last_time = None
   
           if isinstance(state, dict) and state.get("last_event_time"):
               try:
                   last_time = parse_datetime(state["last_event_time"])
                   # Overlap by 2 minutes to catch any delayed events
                   last_time = last_time - timedelta(minutes=2)
               except Exception as e:
                   print(f"Warning: Could not parse last_event_time: {e}")
   
           if last_time is None:
               last_time = now - timedelta(hours=LOOKBACK_HOURS)
   
           print(f"Fetching logs from {last_time.isoformat()} to {now.isoformat()}")
   
           # Fetch checks
           checks, newest_check_time = fetch_checks(
               api_token=ONFIDO_API_TOKEN,
               start_time=last_time,
               end_time=now,
               page_size=PAGE_SIZE,
               max_records=MAX_RECORDS,
           )
   
           # Fetch reports for each check
           all_records = []
           for check in checks:
               check_record = check.copy()
               check_id = check.get('id')
               if check_id:
                   reports = fetch_reports(api_token=ONFIDO_API_TOKEN, check_id=check_id)
                   check_record['reports'] = reports
               all_records.append(check_record)
   
           if not all_records:
               print("No new log records found.")
               save_state(bucket, STATE_KEY, now.isoformat())
               return
   
           # Write to GCS as NDJSON
           timestamp = now.strftime('%Y%m%d_%H%M%S')
           object_key = f"{GCS_PREFIX}/logs_{timestamp}.ndjson"
           blob = bucket.blob(object_key)
   
           ndjson = '\n'.join([json.dumps(record, ensure_ascii=False) for record in all_records]) + '\n'
           blob.upload_from_string(ndjson, content_type='application/x-ndjson')
   
           print(f"Wrote {len(all_records)} records to gs://{GCS_BUCKET}/{object_key}")
   
           # Update state with newest event time
           if newest_check_time:
               save_state(bucket, STATE_KEY, newest_check_time)
           else:
               save_state(bucket, STATE_KEY, now.isoformat())
   
           print(f"Successfully processed {len(all_records)} records")
   
       except Exception as e:
           print(f'Error processing logs: {str(e)}')
           raise
   
   def load_state(bucket, key):
       """Load state from GCS."""
       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: {e}")
   
       return {}
   
   def save_state(bucket, key, last_event_time_iso: str):
       """Save the last event timestamp to GCS state file."""
       try:
           state = {'last_event_time': last_event_time_iso}
           blob = bucket.blob(key)
           blob.upload_from_string(
               json.dumps(state, indent=2),
               content_type='application/json'
           )
           print(f"Saved state: last_event_time={last_event_time_iso}")
       except Exception as e:
           print(f"Warning: Could not save state: {e}")
   
   def fetch_checks(api_token: str, start_time: datetime, end_time: datetime, page_size: int, max_records: int):
       """
       Fetch verification checks from the Onfido API with pagination and rate limiting.
   
       Args:
           api_token: Onfido API token
           start_time: Start time for check query
           end_time: End time for check query
           page_size: Number of records per page
           max_records: Maximum total records to fetch
   
       Returns:
           Tuple of (checks list, newest_event_time ISO string)
       """
       headers = {
           'Authorization': f'Token token={api_token}',
           'Accept': 'application/json',
           'User-Agent': 'GoogleSecOps-OnfidoCollector/1.0'
       }
   
       records = []
       newest_time = None
       page_num = 0
       backoff = 1.0
       current_page = 1
   
       while True:
           page_num += 1
   
           if len(records) >= max_records:
               print(f"Reached max_records limit ({max_records})")
               break
   
           url = f"{API_BASE}/checks?page={current_page}&per_page={page_size}"
   
           try:
               response = http.request('GET', url, headers=headers)
   
               # Handle rate limiting with exponential backoff
               if response.status == 429:
                   retry_after = int(response.headers.get('Retry-After', str(int(backoff))))
                   print(f"Rate limited (429). Retrying after {retry_after}s...")
                   time.sleep(retry_after)
                   backoff = min(backoff * 2, 30.0)
                   continue
   
               backoff = 1.0
   
               if response.status != 200:
                   print(f"HTTP Error: {response.status}")
                   response_text = response.data.decode('utf-8')
                   print(f"Response body: {response_text}")
                   return [], None
   
               data = json.loads(response.data.decode('utf-8'))
   
               page_results = data.get('checks', [])
   
               if not page_results:
                   print(f"No more results (empty page)")
                   break
   
               # Filter checks within the time window
               filtered = []
               for check in page_results:
                   created_at = check.get('created_at')
                   if created_at:
                       try:
                           check_time = parse_datetime(created_at)
                           if start_time <= check_time <= end_time:
                               filtered.append(check)
                           if newest_time is None or check_time > parse_datetime(newest_time):
                               newest_time = created_at
                       except Exception as e:
                           print(f"Warning: Could not parse check time: {e}")
                           filtered.append(check)
   
               print(f"Page {page_num}: Retrieved {len(page_results)} checks, {len(filtered)} in time window")
               records.extend(filtered)
   
               # Check for more results
               if len(page_results) < page_size:
                   print(f"Reached last page (size={len(page_results)} < limit={page_size})")
                   break
   
               current_page += 1
   
           except Exception as e:
               print(f"Error fetching checks: {e}")
               return [], None
   
       print(f"Retrieved {len(records)} total checks from {page_num} pages")
       return records[:max_records], newest_time
   
   def fetch_reports(api_token: str, check_id: str):
       """
       Fetch reports for a specific check from the Onfido API.
   
       Args:
           api_token: Onfido API token
           check_id: Check ID to fetch reports for
   
       Returns:
           List of report objects
       """
       headers = {
           'Authorization': f'Token token={api_token}',
           'Accept': 'application/json',
           'User-Agent': 'GoogleSecOps-OnfidoCollector/1.0'
       }
   
       url = f"{API_BASE}/reports?check_id={check_id}"
   
       try:
           response = http.request('GET', url, headers=headers)
   
           if response.status == 429:
               time.sleep(2)
               response = http.request('GET', url, headers=headers)
   
           if response.status != 200:
               print(f"Error fetching reports for check {check_id}: HTTP {response.status}")
               return []
   
           data = json.loads(response.data.decode('utf-8'))
           reports = data.get('reports', [])
           return reports
   
       except Exception as e:
           print(f"Error fetching reports for check {check_id}: {e}")
           return []
   

   • Second file: requirements.txt:

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

   Note: This script is provided as an example and is not officially supported. We welcome feedback about how we can improve it. To submit feedback, click Send Feedback > Google SecOps Security Product Feedback on this page.

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 will publish 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	onfido-logs-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 onfido-logs-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 onfido-logs-collector.
6. Click the Logs tab.

7. Verify the function executed successfully. Look for:

   Fetching logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
   Page 1: Retrieved X checks, X in time window
   Wrote X records to gs://bucket-name/onfido-logs/logs_YYYYMMDD_HHMMSS.ndjson
   Successfully processed X records
   

8. Go to Cloud Storage > Buckets.

9. Click your bucket name.

10. Navigate to the onfido-logs/ folder.

11. Verify that a new .ndjson file was created with the current timestamp.

If you see errors in the logs:

• HTTP 401: Check the Onfido API token in environment variables
• HTTP 403: Verify the API token has live access and is not revoked
• HTTP 429: Rate limiting - function will automatically retry with backoff
• Missing environment variables: Check all required variables are set

Configure a feed in Google SecOps to ingest Onfido 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, Onfido Verification Logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select Onfido as the Log type.

7. Click Get Service Account. A unique service account email will be displayed, for example:

   [e-mail]
   

8. Copy this email address. You will use it in the next step.

   Note: Each Google SecOps instance has a unique service account. Do not use service accounts from other documentation or examples.

9. Click Next.

10. Specify values for the following input parameters:

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

      gs://onfido-verification-logs/onfido-logs/
      

      • Replace:
        • onfido-verification-logs: Your GCS bucket name.
        • onfido-logs: 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.

11. Click Next.

12. 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.

1. Go to Cloud Storage > Buckets.
2. Click 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.

UDM mapping table

Log Field	UDM Mapping	Logic
	read_only_udm.metadata.vendor_name	Set to "ONFIDO".
	read_only_udm.metadata.product_name	Set to "ONFIDO".
	read_only_udm.metadata.log_type	Set to "ONFIDO".

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