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    Collect OpenAI Audit Logs

    Supported in:

    This document explains how to ingest OpenAI Audit Logs to Google Security Operations using Google Cloud Storage V2.

    OpenAI provides the Audit Logs API for organizations using the OpenAI API Platform. The API tracks user actions and configuration changes within an organization, including API key lifecycle events, user management, project changes, invitations, service account activity, login and logout events, and organization configuration changes.

    Before you begin

    Ensure that you have the following prerequisites:

    • A Google SecOps instance
    • A GCP project with Cloud Storage, Cloud Run, Pub/Sub, and Cloud Scheduler APIs 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
    • Organization Owner role in your OpenAI API Platform organization

    Enable OpenAI audit logging

    Before you can access audit logs, you must enable audit logging in your organization.

    1. Sign in to the OpenAI Platformat https://platform.openai.com .
    2. Go to Settings > Organization > Data controls.
    3. Scroll down to the Audit loggingsection.
    4. Click Enableunder Audit logging.

    5. Click Save.

    Create OpenAI Admin API key

    1. Sign in to the OpenAI Platformat https://platform.openai.com .
    2. In the left-hand panel, click Admin keys.
    3. Alternatively, navigate directly to https://platform.openai.com/settings/organization/admin-keys .
    4. Click Create new admin key.
    5. In the Namefield, enter a descriptive name (for example, Google SecOps Integration ).
    6. Click Create.

    7. Copy the API key immediately and store it securely.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual Admin API key 
 OPENAI_ADMIN_KEY 
 = 
 "your-admin-api-key" 
 # Test audit logs API access 
curl  
-s  
-H  
 "Authorization: Bearer 
 ${ 
 OPENAI_ADMIN_KEY 
 } 
 " 
  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
 "https://api.openai.com/v1/organization/audit_logs?limit=5" 
  
 \ 
  
 | 
  
python3  
-m  
json.tool

    A successful response returns a JSON object with an object field set to list and a data array containing audit log entries.

    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, openai-auditlog-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.

    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 openai-auditlog-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect OpenAI 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.

    Grant IAM permissions on GCS bucket

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

    Create Pub/Sub topic

    1. In the GCP Console, go to Pub/Sub > Topics.
    2. Click Create topic.
    3. Provide the following configuration details:
      • Topic ID: Enter openai-auditlog-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 logs from the OpenAI Audit Logs 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 openai-auditlog-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 openai-auditlog-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 openai-auditlog-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
      		openai-auditlog-logs GCS bucket name
      GCS_PREFIX
      		openai-auditlog Prefix for log files
      STATE_KEY
      		openai-auditlog/state.json State file path
      OPENAI_ADMIN_KEY
      		your-admin-api-key OpenAI Admin API key
      MAX_RECORDS
      		5000 Max records per run
      PAGE_SIZE
      		100 Records per API page (max 100)
      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

    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:

      • main.py:

          import 
  
 functions_framework 
 from 
  
 google.cloud 
  
 import 
  storage 
 
 import 
  
 json 
 import 
  
 os 
 import 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 , 
 timedelta 
 import 
  
 time 
 http 
 = 
 urllib3 
 . 
 PoolManager 
 ( 
 timeout 
 = 
 urllib3 
 . 
 Timeout 
 ( 
 connect 
 = 
 5.0 
 , 
 read 
 = 
 30.0 
 ), 
 retries 
 = 
 False 
 , 
 ) 
 storage_client 
 = 
  storage 
 
 . 
  Client 
 
 () 
 GCS_BUCKET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 GCS_PREFIX 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'openai-auditlog' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'openai-auditlog/state.json' 
 ) 
 OPENAI_ADMIN_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'OPENAI_ADMIN_KEY' 
 ) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '5000' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '100' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '24' 
 )) 
 API_BASE 
 = 
 'https://api.openai.com' 
 AUDIT_LOGS_ENDPOINT 
 = 
 '/v1/organization/audit_logs' 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch OpenAI audit logs 
 and write them to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 OPENAI_ADMIN_KEY 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 GCS_BUCKET 
 ) 
 state 
 = 
 load_state 
 ( 
 bucket 
 ) 
 now 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 if 
 isinstance 
 ( 
 state 
 , 
 dict 
 ) 
 and 
  state 
 
 . 
 get 
 ( 
 'last_effective_at' 
 ): 
 try 
 : 
 last_effective_at 
 = 
 int 
 ( 
 state 
 [ 
 'last_effective_at' 
 ]) 
 last_time 
 = 
 datetime 
 . 
 fromtimestamp 
 ( 
 last_effective_at 
 , 
 tz 
 = 
 timezone 
 . 
 utc 
 ) 
 last_time 
 = 
 last_time 
 - 
 timedelta 
 ( 
 minutes 
 = 
 2 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse last_effective_at: 
 { 
 e 
 } 
 " 
 ) 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 else 
 : 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 start_unix 
 = 
 int 
 ( 
 last_time 
 . 
 timestamp 
 ()) 
 end_unix 
 = 
 int 
 ( 
 now 
 . 
 timestamp 
 ()) 
 print 
 ( 
 f 
 "Fetching audit logs from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 records 
 , 
 newest_effective_at 
 = 
 fetch_audit_logs 
 ( 
 start_unix 
 = 
 start_unix 
 , 
 end_unix 
 = 
 end_unix 
 , 
 page_size 
 = 
 PAGE_SIZE 
 , 
 max_records 
 = 
 MAX_RECORDS 
 , 
 ) 
 if 
 not 
 records 
 : 
 print 
 ( 
 "No new audit log records found." 
 ) 
 save_state 
 ( 
 bucket 
 , 
 end_unix 
 ) 
 return 
 timestamp 
 = 
 now 
 . 
 strftime 
 ( 
 '%Y%m 
 %d 
 _%H%M%S' 
 ) 
 object_key 
 = 
 f 
 " 
 { 
 GCS_PREFIX 
 } 
 /openai_auditlog_ 
 { 
 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 
 } 
 " 
 ) 
 if 
 newest_effective_at 
 : 
 save_state 
 ( 
 bucket 
 , 
 newest_effective_at 
 ) 
 else 
 : 
 save_state 
 ( 
 bucket 
 , 
 end_unix 
 ) 
 print 
 ( 
 f 
 "Successfully processed 
 { 
 len 
 ( 
 records 
 ) 
 } 
 records" 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing audit logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 load_state 
 ( 
 bucket 
 ): 
  
 """Load state from GCS.""" 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 STATE_KEY 
 ) 
 if 
 blob 
 . 
 exists 
 (): 
 return 
 json 
 . 
 loads 
 ( 
 blob 
 . 
  download_as_text 
 
 ()) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not load state: 
 { 
 e 
 } 
 " 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 last_effective_at 
 ): 
  
 """Save the last effective_at Unix timestamp to GCS state file.""" 
 try 
 : 
 state 
 = 
 { 
 'last_effective_at' 
 : 
 last_effective_at 
 , 
 'last_run' 
 : 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 . 
 isoformat 
 () 
 } 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 STATE_KEY 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 , 
 indent 
 = 
 2 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 print 
 ( 
 f 
 "Saved state: last_effective_at= 
 { 
 last_effective_at 
 } 
 " 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not save state: 
 { 
 e 
 } 
 " 
 ) 
 def 
  
 fetch_audit_logs 
 ( 
 start_unix 
 , 
 end_unix 
 , 
 page_size 
 , 
 max_records 
 ): 
  
 """ 
 Fetch audit logs from the OpenAI API with pagination and rate limiting. 
 Args: 
 start_unix: Start time as Unix seconds 
 end_unix: End time as Unix seconds 
 page_size: Number of records per page (max 100) 
 max_records: Maximum total records to fetch 
 Returns: 
 Tuple of (records list, newest effective_at Unix timestamp) 
 """ 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 OPENAI_ADMIN_KEY 
 } 
 ' 
 , 
 'Content-Type' 
 : 
 'application/json' 
 , 
 } 
 records 
 = 
 [] 
 newest_effective_at 
 = 
 None 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 cursor 
 = 
 None 
 while 
 True 
 : 
 page_num 
 += 
 1 
 if 
 len 
 ( 
 records 
 ) 
> = 
 max_records 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 max_records 
 } 
 )" 
 ) 
 break 
 params 
 = 
 [] 
 params 
 . 
 append 
 ( 
 f 
 "effective_at[gte]= 
 { 
 start_unix 
 } 
 " 
 ) 
 params 
 . 
 append 
 ( 
 f 
 "effective_at[lte]= 
 { 
 end_unix 
 } 
 " 
 ) 
 params 
 . 
 append 
 ( 
 f 
 "limit= 
 { 
 min 
 ( 
 page_size 
 , 
  
 max_records 
  
 - 
  
 len 
 ( 
 records 
 )) 
 } 
 " 
 ) 
 if 
 cursor 
 : 
 params 
 . 
 append 
 ( 
 f 
 "after= 
 { 
 cursor 
 } 
 " 
 ) 
 url 
 = 
 f 
 " 
 { 
 API_BASE 
 }{ 
 AUDIT_LOGS_ENDPOINT 
 } 
 ? 
 { 
 '&' 
 . 
 join 
 ( 
 params 
 ) 
 } 
 " 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 headers 
 = 
 headers 
 ) 
 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 
 ( 
 '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 
 ) 
 for 
 event 
 in 
 page_results 
 : 
 try 
 : 
 effective_at 
 = 
 event 
 . 
 get 
 ( 
 'effective_at' 
 ) 
 if 
 effective_at 
 is 
 not 
 None 
 : 
 if 
 newest_effective_at 
 is 
 None 
 or 
 effective_at 
> newest_effective_at 
 : 
 newest_effective_at 
 = 
 effective_at 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse event time: 
 { 
 e 
 } 
 " 
 ) 
 has_more 
 = 
 data 
 . 
 get 
 ( 
 'has_more' 
 , 
 False 
 ) 
 if 
 not 
 has_more 
 : 
 print 
 ( 
 "No more pages (has_more=false)" 
 ) 
 break 
 last_id 
 = 
 data 
 . 
 get 
 ( 
 'last_id' 
 ) 
 if 
 not 
 last_id 
 : 
 print 
 ( 
 "No more pages (no last_id)" 
 ) 
 break 
 cursor 
 = 
 last_id 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching audit logs: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total records from 
 { 
 page_num 
 } 
 pages" 
 ) 
 return 
 records 
 , 
 newest_effective_at

      • 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 Cloud Scheduler job

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

    3. Provide the following configuration details:

      Setting Value
      Name openai-auditlog-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 openai-auditlog-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 ( openai-auditlog-collector-hourly ).
    2. Click Force runto trigger the job manually.
    3. Wait a few seconds.
    4. Go to Cloud Run > Services.
    5. Click on openai-auditlog-collector .
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for:

       Fetching audit 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://openai-auditlog-logs/openai-auditlog/openai_auditlog_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click on openai-auditlog-logs .

    10. Navigate to the openai-auditlog/ folder.

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

    If you see errors in the logs:

    • HTTP 401: Verify the OPENAI_ADMIN_KEY environment variable is correct and the key has not been revoked
    • HTTP 403: Verify the API key is an Admin API key created by an Organization Owner
    • HTTP 429: Rate limiting — the function will automatically retry with exponential backoff
    • Missing environment variables: Verify all required variables are set in the Cloud Run function configuration
    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, OpenAI Audit Logs ).
    5. Select Google Cloud Storage V2as the Source type.

    6. Select OpenAI Audit Logsas the Log type. Click Get Service Account. A unique service account email is displayed. For example:

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

    7. Copy this email address for use in the next step.

    8. Click Next.

    9. Specify values for the following input parameters:

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

         gs://openai-auditlog-logs/openai-auditlog/
      • 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

    10. Click Next.

    11. Review your new feed configuration in the Finalizescreen, and then click Submit.

    1. Go to Cloud Storage > Buckets.
    2. Click on openai-auditlog-logs .
    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
    actor.session.ip_address_details.region_code
    		 additional.fields Merged as key-value pairs
    actor.session.ip_address_details.asn
    		 additional.fields
    effective_at
    		 metadata.event_timestamp Converted from UNIX timestamp
    has_principal
    		 metadata.event_type Set to "STATUS_UPDATE" if has_principal is true; "NETWORK_CONNECTION" if has_principal and has_target are true; "FILE_COPY" if has_principal, has_file, and has_source_file are true; "FILE_UNCATEGORIZED" if has_principal and has_file are true; "USER_UNCATEGORIZED" if has_user is true; else "GENERIC_EVENT"
    has_user
    		 metadata.event_type
    has_target
    		 metadata.event_type
    has_file
    		 metadata.event_type
    has_source_file
    		 metadata.event_type
    actor.session.user_agent
    		 network.http.parsed_user_agent Value copied directly and converted to parsed user agent
    actor.session.ja3
    		 network.tls.client.ja3 Value copied directly
    actor.session.ip_address
    		 principal.asset.ip Value copied directly
    actor.session.ip_address
    		 principal.ip Value copied directly
    actor.session.ip_address_details.city
    		 principal.location.city Value copied directly
    actor.session.ip_address_details.country
    		 principal.location.country_or_region Value copied directly
    actor.session.ip_address_details.latitude
    		 principal.location.region_latitude Converted to float
    actor.session.ip_address_details.longitude
    		 principal.location.region_longitude Converted to float
    actor.session.ip_address_details.region
    		 principal.location.state Value copied directly
    actor.session.user.email
    		 principal.user.email_addresses Value copied directly
    actor.session.user.id
    		 principal.user.userid Extracted using grok pattern
    actor.session.ja4
    		 security_result.detection_fields Merged as key-value pair with key "ja4"
    type
    		 security_result.summary Value copied directly
    metadata.product_name
    		 metadata.product_name Set to "OPENAI_AUDITLOG"
    metadata.vendor_name
    		 metadata.vendor_name Set to "OPENAI_AUDITLOG"

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

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