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    Collect D3 Banking logs

    Supported in:

    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 Applicationor 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 URLfor your tenant (for example, https://api.d3banking.com/v1 or a tenant-specific URL).

    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

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

    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 projectsection, 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 Permissionstab.
    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 Configuresection, 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 3.12or 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 Authenticationsection:

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

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

    8. Go to the Securitytab:

      • Service account: Select the service account d3-banking-logs-collector-sa .

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor 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 & Secretssection, scroll down to Requests:

      • Request timeout: Enter 600 seconds (10 minutes)

    11. Go to the Settingstab:

      • In the Resourcessection:
        • Memory: Select 512 MiBor higher
        • CPU: Select 1

    12. In the Revision scalingsection:

      • 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 editorwill open automatically.

    Add function code

    1. Enter mainin the Entry pointfield.

    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- 
 %d 
 T%H:%M:%SZ' 
 ), 
 'endDate' 
 : 
 end_time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 T%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 Deployto save and deploy the function.

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

    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 Schedulerconsole, find your job.
    2. Click Force runto 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 Logstab.

    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 namefield, enter a name for the feed (for example, D3 Banking Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select D3 Bankingas the Log type.

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

       chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com

    8. Copy this email address.

    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).

      • 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.

      • 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 Finalizescreen, and then click Submit.

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

    1. Go to Cloud Storage > Buckets.
    2. Click on your bucket name.
    3. Go to the Permissionstab.
    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.

    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

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

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