Collect D3 Banking logs

Supported in:

Google secops SIEM

This document explains how to ingest D3 Banking logs to Google Security Operations using Google Cloud Storage V2.

D3 Banking (now NCR Voyix Digital Banking) is a cloud-hosted digital banking platform that generates audit and transaction logs for online banking operations, user sessions, and administrative actions. The NCR Voyix Digital Banking REST API provides programmatic access to audit logs and event data.

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
• Privileged access to the D3 Banking (NCR Voyix) platform with administrator role
• OAuth2 credentials (client ID and client secret) for the NCR Voyix Digital Banking API

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, d3-banking-audit-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.

Collect D3 Banking API credentials

Obtain OAuth2 credentials

1. Sign in to the D3 Banking (NCR Voyix) admin portal.
2. Navigate to Administration > API Management (or Settings > Integrations).
3. Click Register Application or Create API Client.
4. Enter a name for the application (for example, Google Security Operations Integration).
5. Note the following credentials:
   • Client ID: The OAuth2 client identifier
   • Client Secret: The OAuth2 client secret

6. Note the API base URL for your tenant (for example, https://api.d3banking.com/v1 or a tenant-specific URL).

   Note: Contact your NCR Voyix account representative if the API management section is not available or if you need OAuth2 credentials provisioned for your account.

Verify API access

• Test your credentials before proceeding with the integration:

  # Replace with your actual credentials
  CLIENT_ID="your-client-id"
  CLIENT_SECRET="your-client-secret"
  D3_BASE="https://api.d3banking.com/v1"
  
  # Obtain access token
  TOKEN=$(curl -s -X POST "${D3_BASE}/oauth/token" \
      -H "Content-Type: application/x-www-form-urlencoded" \
      -d "grant_type=client_credentials&client_id=${CLIENT_ID}&client_secret=${CLIENT_SECRET}" \
      | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")
  
  # Test audit log access
  curl -s -H "Authorization: Bearer ${TOKEN}" \
      "${D3_BASE}/audit-logs?limit=1" | head -c 500
  

Create a service account for the 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 the 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 d3-banking-logs-collector-sa
   • Service account description: Enter Service account for Cloud Run function to collect D3 Banking 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 (for example, d3-banking-audit-logs).
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, d3-banking-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 d3-banking-logs-trigger
   • Leave other settings as default
4. Click Create.

Create a Cloud Run function to collect logs

The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from the NCR Voyix Digital Banking REST 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	d3-banking-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 the topic d3-banking-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 the service account d3-banking-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	d3-banking-audit-logs	GCS bucket name
   GCS_PREFIX	d3banking	Prefix for log files
   STATE_KEY	d3banking/state.json	State file path
   D3_API_BASE	https://api.d3banking.com/v1	D3 Banking API base URL
   D3_CLIENT_ID	your-client-id	OAuth2 client ID
   D3_CLIENT_SECRET	your-client-secret	OAuth2 client secret
   MAX_RECORDS	5000	Max records per run
   PAGE_SIZE	1000	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 the Entry point field.

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
     import base64
     
     # 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', 'd3banking')
     STATE_KEY = os.environ.get('STATE_KEY', 'd3banking/state.json')
     D3_API_BASE = os.environ.get('D3_API_BASE', 'https://api.d3banking.com/v1')
     D3_CLIENT_ID = os.environ.get('D3_CLIENT_ID')
     D3_CLIENT_SECRET = os.environ.get('D3_CLIENT_SECRET')
     MAX_RECORDS = int(os.environ.get('MAX_RECORDS', '5000'))
     PAGE_SIZE = int(os.environ.get('PAGE_SIZE', '1000'))
     LOOKBACK_HOURS = int(os.environ.get('LOOKBACK_HOURS', '24'))
     
     def to_unix_millis(dt: datetime) -> int:
       """Convert datetime to Unix epoch milliseconds."""
       if dt.tzinfo is None:
         dt = dt.replace(tzinfo=timezone.utc)
       dt = dt.astimezone(timezone.utc)
       return int(dt.timestamp() * 1000)
     
     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)
     
     def get_access_token():
       """
       Obtain an OAuth2 access token using client credentials.
       """
       api_base = D3_API_BASE.rstrip('/')
       token_url = f"{api_base}/oauth/token"
     
       headers = {
         'Content-Type': 'application/x-www-form-urlencoded',
         'Accept': 'application/json'
       }
     
       body = f"grant_type=client_credentials&client_id={D3_CLIENT_ID}&client_secret={D3_CLIENT_SECRET}"
     
       backoff = 1.0
       for attempt in range(3):
         response = http.request('POST', token_url, body=body, headers=headers)
     
         if response.status == 429:
           retry_after = int(response.headers.get('Retry-After', str(int(backoff))))
           print(f"Rate limited (429) on token request. Retrying after {retry_after}s...")
           time.sleep(retry_after)
           backoff = min(backoff * 2, 30.0)
           continue
     
         if response.status != 200:
           raise RuntimeError(f"Failed to get access token: {response.status} - {response.data.decode('utf-8')}")
     
         data = json.loads(response.data.decode('utf-8'))
         return data['access_token']
     
       raise RuntimeError("Failed to get access token after 3 retries")
     
     @functions_framework.cloud_event
     def main(cloud_event):
       """
       Cloud Run function triggered by Pub/Sub to fetch D3 Banking
       audit logs and write to GCS.
     
       Args:
         cloud_event: CloudEvent object containing Pub/Sub message
       """
     
       if not all([GCS_BUCKET, D3_CLIENT_ID, D3_CLIENT_SECRET]):
         print('Error: Missing required environment variables')
         return
     
       try:
         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()}")
     
         # Get access token
         token = get_access_token()
     
         # Fetch audit logs
         records, newest_event_time = fetch_logs(
           token=token,
           start_time=last_time,
           end_time=now,
           page_size=PAGE_SIZE,
           max_records=MAX_RECORDS,
         )
     
         if not 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 records]) + '\n'
         blob.upload_from_string(ndjson, content_type='application/x-ndjson')
     
         print(f"Wrote {len(records)} records to gs://{GCS_BUCKET}/{object_key}")
     
         # Update state with newest event time
         if newest_event_time:
           save_state(bucket, STATE_KEY, newest_event_time)
         else:
           save_state(bucket, STATE_KEY, now.isoformat())
     
         print(f"Successfully processed {len(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_logs(token: str, start_time: datetime, end_time: datetime, page_size: int, max_records: int):
       """
       Fetch audit logs from NCR Voyix Digital Banking REST API
       with pagination and rate limiting.
     
       Args:
         token: OAuth2 access token
         start_time: Start time for log query
         end_time: End time for log query
         page_size: Number of records per page
         max_records: Maximum total records to fetch
     
       Returns:
         Tuple of (records list, newest_event_time ISO string)
       """
       api_base = D3_API_BASE.rstrip('/')
       endpoint = f"{api_base}/audit-logs"
     
       headers = {
         'Authorization': f'Bearer {token}',
         'Accept': 'application/json',
         'User-Agent': 'GoogleSecOps-D3BankingCollector/1.0'
       }
     
       records = []
       newest_time = None
       page_num = 0
       backoff = 1.0
       offset = 0
     
       while True:
         page_num += 1
     
         if len(records) >= max_records:
           print(f"Reached max_records limit ({max_records})")
           break
     
         # Build query parameters
         params = {
           'startDate': start_time.strftime('%Y-%m-%dT%H:%M:%SZ'),
           'endDate': end_time.strftime('%Y-%m-%dT%H:%M:%SZ'),
           'limit': min(page_size, max_records - len(records)),
           'offset': offset
         }
     
         query_string = '&'.join(f"{k}={v}" for k, v in params.items())
         url = f"{endpoint}?{query_string}"
     
         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('auditLogs', data.get('events', data.get('data', [])))
     
           if not page_results:
             print(f"No more results (empty page)")
             break
     
           print(f"Page {page_num}: Retrieved {len(page_results)} events")
           records.extend(page_results)
     
           # Track newest event time
           for event in page_results:
             try:
               event_ts = event.get('timestamp') or event.get('created') or event.get('eventDate')
               if event_ts:
                 if isinstance(event_ts, (int, float)):
                   event_dt = datetime.fromtimestamp(event_ts / 1000, tz=timezone.utc)
                   event_time = event_dt.isoformat()
                 else:
                   event_time = str(event_ts)
                 if newest_time is None or parse_datetime(event_time) > parse_datetime(newest_time):
                   newest_time = event_time
             except Exception as e:
               print(f"Warning: Could not parse event time: {e}")
     
           # Check for more results
           offset += len(page_results)
           if len(page_results) < page_size:
             print("No more pages (partial page received)")
             break
     
         except Exception as e:
           print(f"Error fetching logs: {e}")
           return [], None
     
       print(f"Retrieved {len(records)} total records from {page_num} pages")
       return records, newest_time
     

   • 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 a 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	d3-banking-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 the topic d3-banking-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 d3-banking-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 events
   Wrote X records to gs://d3-banking-audit-logs/d3banking/logs_YYYYMMDD_HHMMSS.ndjson
   Successfully processed X records
   

8. Go to Cloud Storage > Buckets.

9. Click on your bucket name (d3-banking-audit-logs).

10. Navigate to the d3banking/ folder.

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

If you see errors in the logs:

• HTTP 401: Check OAuth2 credentials in environment variables
• HTTP 403: Verify account has required administrator permissions in D3 Banking admin portal
• 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 D3 Banking 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, D3 Banking Logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select D3 Banking 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.

   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://d3-banking-audit-logs/d3banking/
      

      • Replace:
        • d3-banking-audit-logs: Your GCS bucket name.
        • d3banking: 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 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.

UDM mapping table

Log Field	UDM Mapping	Logic
USER_DEVICE_TOKEN_label	additional.fields	Merged
actingProfileType_label	additional.fields	Merged
companyId_label	additional.fields	Merged
component_label	additional.fields	Merged
deleted_label	additional.fields	Merged
enrolledInBiometricAuth_label	additional.fields	Merged
errors_label	additional.fields	Merged
eventClass_label	additional.fields	Merged
issue_label	additional.fields	Merged
q_status_label	additional.fields	Merged
settingsSecQuest_label	additional.fields	Merged
shadowAssistUserId_label	additional.fields	Merged
shadowAssistUsername_label	additional.fields	Merged
source_label	additional.fields	Merged
status_label	additional.fields	Merged
topic_label	additional.fields	Merged
mechanism	extensions.auth.mechanism	Merged
LOGIN_SESSION_TYPE	extensions.auth.type	Directly mapped
defined.message	metadata.description	Directly mapped
@timestamp	metadata.event_timestamp	Parsed as ISO8601
event_type	metadata.event_type	Directly mapped
auditId	metadata.product_event_type	Directly mapped
messageId	metadata.product_event_type	Directly mapped
@version	metadata.product_version	Directly mapped
http	network.http	Renamed/mapped
sessionId	network.session_id	Directly mapped
clientIp	principal.ip	Merged
userClass	principal.user.group_identifiers	Merged
username	principal.user.user_display_name	Directly mapped
userId	principal.user.userid	Directly mapped
sec_result	security_result	Merged
application	target.application	Directly mapped
deviceUuid	target.asset_id	Directly mapped
subcomponent	target.file.names	Merged
producerHostname	target.hostname	Directly mapped
producerIp	target.ip	Merged
fullAddress	target.location.name	Directly mapped
resource	target.resource	Renamed/mapped
userId	target.user.userid	Directly mapped
N/A	extensions.auth.type	Constant: AUTHTYPE_UNSPECIFIED
N/A	metadata.event_type	Constant: GENERIC_EVENT
N/A	metadata.product_name	Constant: D3_BANKING
N/A	metadata.vendor_name	Constant: D3_BANKING

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