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    Collect Alveo Risk Data Management logs

    Supported in:

    This document explains how to ingest Alveo Risk Data Management (RDM) logs to Google Security Operations using Google Cloud Storage V2.

    Alveo RDM, part of the Prime EDM platform by Gresham Technologies, is a cloud-based financial data management solution that helps banks, asset managers, insurers, and other financial institutions manage market data, risk factors, and reference data across multiple asset classes. The platform provides a RESTful API through its Ops360 interface for programmatic access to data operations, audit trail records, and data lineage information. These audit and operational logs can be exported through the REST API and written to a GCS bucket for ingestion by Google SecOps.

    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 create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
    • An Alveo RDM (Prime EDM) instance with REST API access enabled
    • An Alveo user account with API credentials and permissions to access audit trail data
    • API client credentials (Client ID and Client Secret) generated from the Alveo administration console

    Collect Alveo RDM API credentials

    Get the Alveo instance details

    1. Sign in to your Alveo RDM(Prime EDM) instance.

    2. Note the following values from the browser address bar:

      • Base URL: The API server address (for example, https://yourcompany.alveotech.com )
      • Tenant ID: Your organization's tenant identifier, displayed in the administration console

    Create API credentials

    1. Sign in to the Alveo RDMadministration console.
    2. Go to Administration > API Access > Client Credentials.
    3. Click Create New Client.
    4. Provide the following configuration details:
      • Client Name: Enter a descriptive name (for example, Google SecOps Integration )
      • Scopes: Select audit:readand data:read
    5. Click Create.

    6. Copy and securely store the generated credentials:

      • Client ID: Copy this value
      • Client Secret: Copy this value

    Verify permissions

    To verify the account has the required permissions:

    1. Sign in to the Alveo RDMconsole with API user credentials.
    2. Go to Administration > Audit Trail.
    3. Verify that audit trail records are visible and accessible.
    4. If you cannot see the Audit Trail section, contact your Alveo administrator to grant the necessary permissions.

    Test API access

    • Test your credentials before proceeding with the integration:

        ALVEO_BASE_URL 
 = 
 "https://yourcompany.alveotech.com" 
 ALVEO_CLIENT_ID 
 = 
 "your-client-id" 
 ALVEO_CLIENT_SECRET 
 = 
 "your-client-secret" 
 # Get OAuth2 access token 
 TOKEN 
 = 
 $( 
curl  
-s  
-X  
POST  
 " 
 ${ 
 ALVEO_BASE_URL 
 } 
 /oauth/token" 
  
 \ 
  
-H  
 "Content-Type: application/x-www-form-urlencoded" 
  
 \ 
  
-d  
 "grant_type=client_credentials&client_id= 
 ${ 
 ALVEO_CLIENT_ID 
 } 
& client_secret= 
 ${ 
 ALVEO_CLIENT_SECRET 
 } 
 " 
  
 \ 
  
 | 
  
python3  
-c  
 "import sys,json; print(json.load(sys.stdin).get('access_token',''))" 
 ) 
 echo 
  
 "Token: 
 ${ 
 TOKEN 
 } 
 " 
 # Test audit trail API access 
curl  
-v  
 " 
 ${ 
 ALVEO_BASE_URL 
 } 
 /api/v1/audit/events?limit=1" 
  
 \ 
  
-H  
 "Authorization: Bearer 
 ${ 
 TOKEN 
 } 
 " 
  
 \ 
  
-H  
 "Accept: application/json"

    Create a 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, alveo-rdm-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.

    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 alveo-rdm-logs-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect Alveo RDM audit 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 the 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 Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email (for example, alveo-rdm-logs-collector-sa@PROJECT_ID.iam.gserviceaccount.com )
      • Assign roles: Select Storage Object Admin
    6. Click Save.

    Create a 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 alveo-rdm-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 audit trail logs from the Alveo RDM 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 alveo-rdm-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 alveo-rdm-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 alveo-rdm-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
      		alveo-rdm-audit-logs GCS bucket name
      GCS_PREFIX
      		alveo-rdm Prefix for log files
      STATE_KEY
      		alveo-rdm/state.json State file path
      ALVEO_BASE_URL
      		https://yourcompany.alveotech.com Alveo RDM API base URL
      ALVEO_CLIENT_ID
      		your-client-id OAuth2 Client ID
      ALVEO_CLIENT_SECRET
      		your-client-secret OAuth2 Client Secret
      PAGE_SIZE
      		100 Records per API page
      MAX_RECORDS
      		10000 Max records per run
      LOOKBACK_HOURS
      		2 Initial lookback period

    10. Scroll down in the Variables & Secretssection 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 
 import 
  
 urllib.parse 
 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' 
 , 
 'alveo-rdm' 
 ) 
 . 
 strip 
 ( 
 '/' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 ) 
 or 
 f 
 " 
 { 
 GCS_PREFIX 
 } 
 /state.json" 
 ALVEO_BASE_URL 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ALVEO_BASE_URL' 
 , 
 '' 
 ) 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 ALVEO_CLIENT_ID 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ALVEO_CLIENT_ID' 
 ) 
 ALVEO_CLIENT_SECRET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ALVEO_CLIENT_SECRET' 
 ) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '100' 
 )) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '10000' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '2' 
 )) 
 def 
  
 parse_datetime 
 ( 
 value 
 ): 
  
 """Parse ISO datetime string to datetime object.""" 
 if 
 not 
 value 
 : 
 return 
 None 
 if 
  value 
 
 . 
 endswith 
 ( 
 "Z" 
 ): 
 value 
 = 
 value 
 [: 
 - 
 1 
 ] 
 + 
 "+00:00" 
 try 
 : 
 return 
 datetime 
 . 
 fromisoformat 
 ( 
 value 
 ) 
 except 
 Exception 
 : 
 return 
 None 
 def 
  
 get_access_token 
 (): 
  
 """Get OAuth2 access token using client credentials.""" 
 token_url 
 = 
 f 
 " 
 { 
 ALVEO_BASE_URL 
 } 
 /oauth/token" 
 body 
 = 
 urllib 
 . 
 parse 
 . 
 urlencode 
 ({ 
 'grant_type' 
 : 
 'client_credentials' 
 , 
 'client_id' 
 : 
 ALVEO_CLIENT_ID 
 , 
 'client_secret' 
 : 
 ALVEO_CLIENT_SECRET 
 , 
 }) 
 . 
 encode 
 ( 
 'utf-8' 
 ) 
 response 
 = 
 http 
 . 
 request 
 ( 
 'POST' 
 , 
 token_url 
 , 
 body 
 = 
 body 
 , 
 headers 
 = 
 { 
 'Content-Type' 
 : 
 'application/x-www-form-urlencoded' 
 }, 
 ) 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 raise 
 Exception 
 ( 
 f 
 "Failed to get access token: HTTP 
 { 
 response 
 . 
 status 
 } 
 " 
 f 
 " 
 { 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 } 
 " 
 ) 
 data 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 return 
 data 
 [ 
 'access_token' 
 ] 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch 
 Alveo RDM audit trail records and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 ALVEO_BASE_URL 
 , 
 ALVEO_CLIENT_ID 
 , 
 ALVEO_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 delayed events 
 if 
 last_time 
 : 
 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 audit logs from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 " 
 f 
 "to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 # Get OAuth2 token 
 token 
 = 
 get_access_token 
 () 
 print 
 ( 
 "Successfully obtained OAuth2 access token" 
 ) 
 # Fetch audit logs 
 records 
 , 
 newest_event_time 
 = 
 fetch_audit_logs 
 ( 
 token 
 = 
 token 
 , 
 start_time 
 = 
 last_time 
 , 
 end_time 
 = 
 now 
 , 
 ) 
 if 
 not 
 records 
 : 
 print 
 ( 
 "No new audit 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 " 
 f 
 "gs:// 
 { 
 GCS_BUCKET 
 } 
 / 
 { 
 object_key 
 } 
 " 
 ) 
 # Update state 
 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 audit 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 
 ): 
  
 """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_audit_logs 
 ( 
 token 
 , 
 start_time 
 , 
 end_time 
 ): 
  
 """ 
 Fetch audit trail logs from Alveo RDM REST API with pagination. 
 Args: 
 token: OAuth2 access token 
 start_time: Start time for log query 
 end_time: End time for log query 
 Returns: 
 Tuple of (records list, newest_event_time ISO string) 
 """ 
 endpoint 
 = 
 f 
 " 
 { 
 ALVEO_BASE_URL 
 } 
 /api/v1/audit/events" 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 token 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'Content-Type' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-AlveoRDMCollector/1.0' 
 } 
 start_iso 
 = 
 start_time 
 . 
 isoformat 
 () 
 end_iso 
 = 
 end_time 
 . 
 isoformat 
 () 
 records 
 = 
 [] 
 newest_time 
 = 
 None 
 offset 
 = 
 0 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 while 
 True 
 : 
 if 
 len 
 ( 
 records 
 ) 
> = 
 MAX_RECORDS 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 MAX_RECORDS 
 } 
 )" 
 ) 
 break 
 params 
 = 
 ( 
 f 
 "?startTime= 
 { 
 urllib 
 . 
 parse 
 . 
 quote 
 ( 
 start_iso 
 ) 
 } 
 " 
 f 
 "&endTime= 
 { 
 urllib 
 . 
 parse 
 . 
 quote 
 ( 
 end_iso 
 ) 
 } 
 " 
 f 
 "&offset= 
 { 
 offset 
 } 
 " 
 f 
 "&limit= 
 { 
 min 
 ( 
 PAGE_SIZE 
 , 
  
 MAX_RECORDS 
  
 - 
  
 len 
 ( 
 records 
 )) 
 } 
 " 
 ) 
 url 
 = 
 f 
 " 
 { 
 endpoint 
 }{ 
 params 
 } 
 " 
 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 
 ( 
 'events' 
 , 
 []) 
 if 
 not 
 page_results 
 : 
 print 
 ( 
 f 
 "No more results (empty page 
 { 
 page_num 
 } 
 )" 
 ) 
 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_time 
 = 
 event 
 . 
 get 
 ( 
 'timestamp' 
 ) 
 if 
 event_time 
 : 
 if 
 newest_time 
 is 
 None 
 : 
 newest_time 
 = 
 event_time 
 else 
 : 
 current_dt 
 = 
 parse_datetime 
 ( 
 event_time 
 ) 
 newest_dt 
 = 
 parse_datetime 
 ( 
 newest_time 
 ) 
 if 
 ( 
 current_dt 
 and 
 newest_dt 
 and 
 current_dt 
> newest_dt 
 ): 
 newest_time 
 = 
 event_time 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse event time: 
 { 
 e 
 } 
 " 
 ) 
 # Check pagination 
 if 
 len 
 ( 
 page_results 
 ) 
< PAGE_SIZE 
 : 
 print 
 ( 
 f 
 "Reached last page " 
 f 
 "(size= 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 < limit= 
 { 
 PAGE_SIZE 
 } 
 )" 
 ) 
 break 
 offset 
 += 
 len 
 ( 
 page_results 
 ) 
 page_num 
 += 
 1 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching audit logs: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total records " 
 f 
 "from 
 { 
 page_num 
  
 + 
  
 1 
 } 
 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 alveo-rdm-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 alveo-rdm-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 hour
    		 0 * * * * Standard (recommended)
    Every 2 hours
    		 0 */2 * * * Lower volume
    Every 6 hours
    		 0 */6 * * * Low volume, batch processing

    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 the function name alveo-rdm-logs-collector .
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for:

       Successfully obtained OAuth2 access token
Fetching audit logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Page 0: Retrieved X events
Wrote X records to gs://alveo-rdm-audit-logs/alveo-rdm/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click your bucket name.

    10. Navigate to the prefix folder alveo-rdm/ .

    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. Verify Client ID and Client Secret are correct.
    • HTTP 403: Verify the API client has the required scopes (audit:read, data:read).
    • HTTP 429: Rate limiting - function will automatically retry with backoff.
    • Token error: Check that the ALVEO_BASE_URL is correct and the OAuth2 token endpoint is reachable.
    • Missing environment variables: Check all required variables are set.

    Configure a feed in Google SecOps to ingest Alveo RDM 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, Alveo RDM Audit Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Alveo Risk Data Managementas 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 for use in the next step.

    9. Click Next.

    10. Specify values for the following input parameters:

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

         gs://alveo-rdm-audit-logs/alveo-rdm/
        • Replace:
          • alveo-rdm-audit-logs : Your GCS bucket name.
          • alveo-rdm : 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 the Storage Object Viewerrole on your GCS bucket.

    1. Go to Cloud Storage > Buckets.
    2. Click 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
    function_label
    		additional.fields Merged
    interface_attribute_label
    		additional.fields Merged
    interface_datafile_label
    		additional.fields Merged
    log_level_label
    		additional.fields Merged
    new_value_label
    		additional.fields Merged
    object_label
    		additional.fields Merged
    old_value_label
    		additional.fields Merged
    operation_type_label
    		additional.fields Merged
    register_type_label
    		additional.fields Merged
    status_process_label
    		additional.fields Merged
    msg
    		extensions.auth.type Mapped: logon → MACHINE
    date
    		metadata.event_timestamp Parsed as MMM dd yyyy HH:mm:ss
    date_hours
    		metadata.event_timestamp Parsed as yyyyMMdd HHmmss
    has_principal
    		metadata.event_type Mapped: true → NETWORK_CONNECTION , true → STATUS_UPDATE
    has_user
    		metadata.event_type Mapped: true → USER_UNCATEGORIZED
    msg
    		metadata.event_type Mapped: logon → USER_LOGIN
    host_name
    		principal.asset.hostname Directly mapped
    src_ip
    		principal.asset.ip Merged
    host_name
    		principal.hostname Directly mapped
    src_ip
    		principal.ip Merged
    country
    		principal.location.country_or_region Directly mapped
    object_type
    		principal.user.role_name Directly mapped
    modified_user
    		principal.user.userid Directly mapped
    act
    		security_result.action Merged
    msg
    		security_result.action Mapped: failed → act
    msg
    		security_result.description Directly mapped
    process_id
    		target.process.pid Directly mapped
    affected_user
    		target.user.userid Directly mapped
    user_id
    		target.user.userid Directly mapped
    N/A
    		 extensions.auth.type Constant: MACHINE
    N/A
    		 metadata.event_type Constant: USER_LOGIN
    N/A
    		 metadata.product_name Constant: ALVEO RDM
    N/A
    		 metadata.vendor_name Constant: ALVEO RDM

    Change Log

    View the Change Log for this parser

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

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