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    Collect Zoom operation logs

    Supported in:

    This document explains how to ingest Zoom operation logs to Google Security Operations using Google Cloud Storage. The parser transforms the raw logs into a unified data model (UDM). It extracts fields from the raw log message, performs data cleaning and normalization, and maps the extracted information to corresponding UDM fields, ultimately enriching the data for analysis and correlation within a SIEM system.

    Before you begin

    Ensure that 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 functions, Pub/Sub topics, and Cloud Scheduler jobs
    • Privileged access to Zoom

    Collect Zoom operation logs prerequisites

    1. Sign in to Zoom App Marketplace .
    2. Go to Develop > Build App > Server-to-Server OAuth.
    3. Create the app and add the following scope: report:read:operation_logs:admin (or report:read:admin ).

    4. In App Credentials, copy and save the following details in a secure location:

      • Account ID
      • Client ID
      • Client Secret

    Verify permissions

    To verify the account has the required permissions:

    1. Sign in to your Zoom account.
    2. Go to Admin > Account Management > Account Profile.
    3. If you can access the account settings and view operation logs, you have the required permissions.
    4. If you cannot access these options, contact your Zoom administrator to grant the necessary permissions.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual credentials 
 ZOOM_ACCOUNT_ID 
 = 
 "<your-account-id>" 
 ZOOM_CLIENT_ID 
 = 
 "<your-client-id>" 
 ZOOM_CLIENT_SECRET 
 = 
 "<your-client-secret>" 
 # Get OAuth token 
 TOKEN 
 = 
 $( 
curl  
-s  
-X  
POST  
 "https://zoom.us/oauth/token?grant_type=account_credentials&account_id= 
 ${ 
 ZOOM_ACCOUNT_ID 
 } 
 " 
  
 \ 
  
-u  
 " 
 ${ 
 ZOOM_CLIENT_ID 
 } 
 : 
 ${ 
 ZOOM_CLIENT_SECRET 
 } 
 " 
  
 \ 
  
 | 
  
grep  
-o  
 '"access_token":"[^"]*"' 
  
 | 
  
cut  
-d '"' 
  
-f4 ) 
 # Test API access 
curl  
-v  
-H  
 "Authorization: Bearer 
 ${ 
 TOKEN 
 } 
 " 
  
 \ 
  
 "https://api.zoom.us/v2/report/operationlogs?from= 
 $( 
date  
-u  
-d  
 '1 day ago' 
  
+%Y-%m-%d ) 
& to= 
 $( 
date  
-u  
+%Y-%m-%d ) 
& page_size=10"

    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, zoom-operation-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 zoom-operationlogs-sa .
      • Service account description: Enter Service account for Cloud Run function to collect Zoom operation 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
    • Storage Object Admin: Write logs to GCS bucket and manage state files
    • Cloud Run Invoker: Allow Pub/Sub to invoke the function
    • Cloud Functions Invoker: Allow function invocation

    Grant IAM permissions on GCS bucket

    Grant the service account write permissions on the GCS bucket:

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name (for example, zoom-operation-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, zoom-operationlogs-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 zoom-operationlogs-trigger .
      • Leave other settings as default.
    4. Click Create.

    Create Cloud Run function to collect logs

    The Cloud Run function is triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from Zoom API and writes 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 zoom-operationlogs-to-gcs
      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 zoom-operationlogs-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 zoom-operationlogs-sa .

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value
      GCS_BUCKET zoom-operation-logs
      GCS_PREFIX zoom/operationlogs/
      STATE_KEY zoom/operationlogs/state.json
      ZOOM_ACCOUNT_ID <your-zoom-account-id>
      ZOOM_CLIENT_ID <your-zoom-client-id>
      ZOOM_CLIENT_SECRET <your-zoom-client-secret>
      PAGE_SIZE 300
      TIMEOUT 30

    10. In the Variables & Secretssection, scroll 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 editoropens automatically.

    Add function code

    1. Enter mainin Function entry point

    2. In the inline code editor, create two files:

      • First file: main.py:
        import 
  
 functions_framework 
 from 
  
 google.cloud 
  
 import 
  storage 
 
 import 
  
 json 
 import 
  
 os 
 import 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 date 
 , 
 timedelta 
 , 
 timezone 
 import 
  
 base64 
 import 
  
 uuid 
 import 
  
 gzip 
 import 
  
 io 
 # 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' 
 , 
 'zoom/operationlogs/' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'zoom/operationlogs/state.json' 
 ) 
 ZOOM_ACCOUNT_ID 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZOOM_ACCOUNT_ID' 
 ) 
 ZOOM_CLIENT_ID 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZOOM_CLIENT_ID' 
 ) 
 ZOOM_CLIENT_SECRET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZOOM_CLIENT_SECRET' 
 ) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '300' 
 )) 
 TIMEOUT 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'TIMEOUT' 
 , 
 '30' 
 )) 
 TOKEN_URL 
 = 
 "https://zoom.us/oauth/token" 
 REPORT_URL 
 = 
 "https://api.zoom.us/v2/report/operationlogs" 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch Zoom operation logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 ZOOM_ACCOUNT_ID 
 , 
 ZOOM_CLIENT_ID 
 , 
 ZOOM_CLIENT_SECRET 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 GCS_BUCKET 
 ) 
 # Get OAuth token 
 token 
 = 
 get_token 
 () 
 # Load state 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 ) 
 cursor_date 
 = 
  state 
 
 . 
 get 
 ( 
 'cursor_date' 
 , 
 date 
 . 
 today 
 () 
 . 
 isoformat 
 ()) 
 print 
 ( 
 f 
 'Processing logs for date: 
 { 
 cursor_date 
 } 
 ' 
 ) 
 # Fetch logs 
 from_date 
 = 
 cursor_date 
 to_date 
 = 
 cursor_date 
 total_written 
 = 
 0 
 next_token 
 = 
  state 
 
 . 
 get 
 ( 
 'next_page_token' 
 ) 
 while 
 True 
 : 
 page 
 = 
 fetch_page 
 ( 
 token 
 , 
 from_date 
 , 
 to_date 
 , 
 next_token 
 ) 
 items 
 = 
 page 
 . 
 get 
 ( 
 'operation_logs' 
 , 
 []) 
 or 
 [] 
 if 
 items 
 : 
 write_chunk 
 ( 
 bucket 
 , 
 items 
 , 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 )) 
 total_written 
 += 
 len 
 ( 
 items 
 ) 
 next_token 
 = 
 page 
 . 
 get 
 ( 
 'next_page_token' 
 ) 
 if 
 not 
 next_token 
 : 
 break 
 # Advance to next day if we've finished this date 
 today 
 = 
 date 
 . 
 today 
 () 
 . 
 isoformat 
 () 
 if 
 cursor_date 
< today 
 : 
 nxt 
 = 
 ( 
 datetime 
 . 
 fromisoformat 
 ( 
 cursor_date 
 ) 
 + 
 timedelta 
 ( 
 days 
 = 
 1 
 )) 
 . 
 date 
 () 
 . 
 isoformat 
 () 
 state 
 [ 
 'cursor_date' 
 ] 
 = 
 nxt 
 state 
 [ 
 'next_page_token' 
 ] 
 = 
 None 
 else 
 : 
 # stay on today; continue later with next_page_token=None 
 state 
 [ 
 'next_page_token' 
 ] 
 = 
 None 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 state 
 ) 
 print 
 ( 
 f 
 'Successfully processed 
 { 
 total_written 
 } 
 logs for 
 { 
 from_date 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 get_token 
 (): 
  
 """Get OAuth 2.0 access token from Zoom.""" 
 params 
 = 
 f 
 "grant_type=account_credentials&account_id= 
 { 
 ZOOM_ACCOUNT_ID 
 } 
 " 
 basic 
 = 
 base64 
 . 
 b64encode 
 ( 
 f 
 " 
 { 
 ZOOM_CLIENT_ID 
 } 
 : 
 { 
 ZOOM_CLIENT_SECRET 
 } 
 " 
 . 
 encode 
 ( 
 'utf-8' 
 )) 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Basic 
 { 
 basic 
 } 
 ' 
 , 
 'Content-Type' 
 : 
 'application/x-www-form-urlencoded' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'Host' 
 : 
 'zoom.us' 
 } 
 response 
 = 
 http 
 . 
 request 
 ( 
 'POST' 
 , 
 TOKEN_URL 
 , 
 body 
 = 
 params 
 , 
 headers 
 = 
 headers 
 , 
 timeout 
 = 
 TIMEOUT 
 ) 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 'Token request failed: 
 { 
 response 
 . 
 status 
 } 
 ' 
 ) 
 response_text 
 = 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 print 
 ( 
 f 
 'Response body: 
 { 
 response_text 
 } 
 ' 
 ) 
 raise 
 Exception 
 ( 
 f 
 'Failed to get OAuth token: 
 { 
 response 
 . 
 status 
 } 
 ' 
 ) 
 body 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 return 
 body 
 [ 
 'access_token' 
 ] 
 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: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 # Initial state: start today 
 today 
 = 
 date 
 . 
 today 
 () 
 . 
 isoformat 
 () 
 return 
 { 
 'cursor_date' 
 : 
 today 
 , 
 'next_page_token' 
 : 
 None 
 } 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 state 
 ): 
  
 """Save state to GCS.""" 
 try 
 : 
 state 
 [ 
 'updated_at' 
 ] 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 . 
 isoformat 
 () 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Warning: Could not save state: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 def 
  
 write_chunk 
 ( 
 bucket 
 , 
 items 
 , 
 ts 
 ): 
  
 """Write log chunk to GCS.""" 
 key 
 = 
 f 
 " 
 { 
 GCS_PREFIX 
 }{ 
 ts 
 : 
 %Y/%m/%d 
 } 
 /zoom-operationlogs- 
 { 
 uuid 
 . 
 uuid4 
 () 
 } 
 .json.gz" 
 buf 
 = 
 io 
 . 
 BytesIO 
 () 
 with 
 gzip 
 . 
 GzipFile 
 ( 
 fileobj 
 = 
 buf 
 , 
 mode 
 = 
 'w' 
 ) 
 as 
 gz 
 : 
 for 
 rec 
 in 
 items 
 : 
 gz 
 . 
 write 
 (( 
 json 
 . 
 dumps 
 ( 
 rec 
 ) 
 + 
 ' 
 \n 
 ' 
 ) 
 . 
 encode 
 ( 
 'utf-8' 
 )) 
 buf 
 . 
 seek 
 ( 
 0 
 ) 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_file 
 
 ( 
 buf 
 , 
 content_type 
 = 
 'application/gzip' 
 ) 
 print 
 ( 
 f 
 'Wrote 
 { 
 len 
 ( 
 items 
 ) 
 } 
 logs to 
 { 
 key 
 } 
 ' 
 ) 
 return 
 key 
 def 
  
 fetch_page 
 ( 
 token 
 , 
 from_date 
 , 
 to_date 
 , 
 next_page_token 
 ): 
  
 """Fetch a page of logs from Zoom API.""" 
 params 
 = 
 { 
 'from' 
 : 
 from_date 
 , 
 'to' 
 : 
 to_date 
 , 
 'page_size' 
 : 
 str 
 ( 
 PAGE_SIZE 
 ) 
 } 
 if 
 next_page_token 
 : 
 params 
 [ 
 'next_page_token' 
 ] 
 = 
 next_page_token 
 # Build query string 
 query_string 
 = 
 '&' 
 . 
 join 
 ([ 
 f 
 " 
 { 
 k 
 } 
 = 
 { 
 v 
 } 
 " 
 for 
 k 
 , 
 v 
 in 
 params 
 . 
 items 
 ()]) 
 url 
 = 
 f 
 " 
 { 
 REPORT_URL 
 } 
 ? 
 { 
 query_string 
 } 
 " 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 token 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 } 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 headers 
 = 
 headers 
 , 
 timeout 
 = 
 TIMEOUT 
 ) 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 'API request failed: 
 { 
 response 
 . 
 status 
 } 
 ' 
 ) 
 response_text 
 = 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 print 
 ( 
 f 
 'Response body: 
 { 
 response_text 
 } 
 ' 
 ) 
 raise 
 Exception 
 ( 
 f 
 'Failed to fetch logs: 
 { 
 response 
 . 
 status 
 } 
 ' 
 ) 
 return 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ))
      • 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 Cloud Scheduler job

    Cloud scheduler publishes 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 zoom-operationlogs-schedule-15min
      Region Select same region as Cloud Run function
      Frequency */15 * * * * (every 15 minutes)
      Timezone Select timezone (UTC recommended)
      Target type Pub/Sub
      Topic Select zoom-operationlogs-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 * * * * Standard (recommended)
      Every hour
      		 0 * * * * Low volume
      Every 6 hours
      		 0 */6 * * * Batch processing

    Test the integration

    1. In the Cloud Schedulerconsole, find your job (for example, zoom-operationlogs-schedule-15min ).
    2. Click Force runto trigger the job manually.
    3. Wait a few seconds.
    4. Go to Cloud Run > Services.
    5. Click the function name ( zoom-operationlogs-to-gcs ).
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for the following:

        Proc 
 essing 
  
 logs 
  
 for 
  
 date 
 : 
  
 YYYY 
 - 
 MM 
 - 
 DD 
 Page 
  
 1 
 : 
  
 Retrieved 
  
 X 
  
 events 
 Wrote 
  
 X 
  
 records 
  
 to 
  
 zoom 
 / 
 operationlogs 
 / 
 YYYY 
 / 
 MM 
 / 
 DD 
 / 
 zoom 
 - 
 operationlogs 
 - 
 UUID.json.gz 
 Successfully 
  
 processed 
  
 X 
  
 logs 
  
 for 
  
 YYYY 
 - 
 MM 
 - 
 DD

    8. Go to Cloud Storage > Buckets.

    9. Click your bucket name ( zoom-operation-logs ).

    10. Navigate to the prefix folder ( zoom/operationlogs/ ).

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

    If you see errors in the logs:

    • HTTP 401: Check Zoom API credentials in environment variables
    • HTTP 403: Verify Zoom app has report:read:operation_logs:admin scope
    • Missing environment variables: Check all required variables are set in Cloud Run function configuration

    Google SecOps uses a unique service account to read data from your GCS bucket. You must grant this service account access to your bucket.

    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, Zoom Operation Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Zoom Operation Logsas the Log type.

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

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

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

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

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name (for example, zoom-operation-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.

    Configure a feed in Google SecOps to ingest Zoom operation 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, Zoom Operation Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Zoom Operation Logsas the Log type.
    7. Click Next.

    8. Specify values for the following input parameters:

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

         gs://zoom-operation-logs/zoom/operationlogs/

        • Replace:

          • zoom-operation-logs : Your GCS bucket name.
          • zoom/operationlogs/ : Prefix path where logs are stored.

      • 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.

    9. Click Next.

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

    UDM mapping table

    Log Field UDM Mapping Logic
    action
    		 metadata.product_event_type The raw log field "action" is mapped to this UDM field.
    category_type
    		 additional.fields.key The raw log field "category_type" is mapped to this UDM field.
    category_type
    		 additional.fields.value.string_value The raw log field "category_type" is mapped to this UDM field.
    Department
    		 target.user.department The raw log field "Department" (extracted from "operation_detail" field) is mapped to this UDM field.
    Description
    		 target.user.role_description The raw log field "Description" (extracted from "operation_detail" field) is mapped to this UDM field.
    Display Name
    		 target.user.user_display_name The raw log field "Display Name" (extracted from "operation_detail" field) is mapped to this UDM field.
    Email Address
    		 target.user.email_addresses The raw log field "Email Address" (extracted from "operation_detail" field) is mapped to this UDM field.
    First Name
    		 target.user.first_name The raw log field "First Name" (extracted from "operation_detail" field) is mapped to this UDM field.
    Job Title
    		 target.user.title The raw log field "Job Title" (extracted from "operation_detail" field) is mapped to this UDM field.
    Last Name
    		 target.user.last_name The raw log field "Last Name" (extracted from "operation_detail" field) is mapped to this UDM field.
    Location
    		 target.location.name The raw log field "Location" (extracted from "operation_detail" field) is mapped to this UDM field.
    operation_detail
    		 metadata.description The raw log field "operation_detail" is mapped to this UDM field.
    operator
    		 principal.user.email_addresses The raw log field "operator" is mapped to this UDM field if it matches an email regex.
    operator
    		 principal.user.userid The raw log field "operator" is mapped to this UDM field if it doesn't match an email regex.
    Room Name
    		 target.user.attribute.labels.value The raw log field "Room Name" (extracted from "operation_detail" field) is mapped to this UDM field.
    Role Name
    		 target.user.attribute.roles.name The raw log field "Role Name" (extracted from "operation_detail" field) is mapped to this UDM field.
    time
    		 metadata.event_timestamp.seconds The raw log field "time" is parsed and mapped to this UDM field.
    Type
    		 target.user.attribute.labels.value The raw log field "Type" (extracted from "operation_detail" field) is mapped to this UDM field.
    User Role
    		 target.user.attribute.roles.name The raw log field "User Role" (extracted from "operation_detail" field) is mapped to this UDM field.
    User Type
    		 target.user.attribute.labels.value The raw log field "User Type" (extracted from "operation_detail" field) is mapped to this UDM field.
    		metadata.log_type The value "ZOOM_OPERATION_LOGS" is assigned to this UDM field.
    		metadata.vendor_name The value "ZOOM" is assigned to this UDM field.
    		metadata.product_name The value "ZOOM_OPERATION_LOGS" is assigned to this UDM field.
    		metadata.event_type The value is determined based on the following logic: 1. If "event_type" field is not empty, its value is used. 1. If "operator", "email", or "email2" fields are not empty, the value is set to "USER_UNCATEGORIZED". 1. Otherwise, the value is set to "GENERIC_EVENT".
    json_data
    		 about.user.attribute.labels.value The raw log field "json_data" (extracted from "operation_detail" field) is parsed as JSON. The "assistant" and "options" fields from each element of the parsed JSON array are mapped to the "value" field of the "labels" array in the UDM.
    json_data
    		 about.user.userid The raw log field "json_data" (extracted from "operation_detail" field) is parsed as JSON. The "userId" field from each element of the parsed JSON array (except the first one) is mapped to the "userid" field of the "about.user" object in the UDM.
    json_data
    		 target.user.attribute.labels.value The raw log field "json_data" (extracted from "operation_detail" field) is parsed as JSON. The "assistant" and "options" fields from the first element of the parsed JSON array are mapped to the "value" field of the "labels" array in the UDM.
    json_data
    		 target.user.userid The raw log field "json_data" (extracted from "operation_detail" field) is parsed as JSON. The "userId" field from the first element of the parsed JSON array is mapped to the "userid" field of the "target.user" object in the UDM.
    email
    		 target.user.email_addresses The raw log field "email" (extracted from "operation_detail" field) is mapped to this UDM field.
    email2
    		 target.user.email_addresses The raw log field "email2" (extracted from "operation_detail" field) is mapped to this UDM field.
    role
    		 target.user.attribute.roles.name The raw log field "role" (extracted from "operation_detail" field) is mapped to this UDM field.

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