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    Collect TeamViewer logs

    Supported in:

    This document explains how to ingest TeamViewer logs to Google Security Operations using Google Cloud Storage. The parser extracts the audit events from JSON formatted logs. It iterates through event details, mapping specific properties to Unified Data Model (UDM) fields, handling participant and presenter information, and categorizing events based on user activity. The parser also performs data transformations, such as merging labels and converting timestamps to a standardized format.

    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 services, Pub/Sub topics, and Cloud Scheduler jobs
    • Privileged access to TeamViewer Management Console
    • TeamViewer Business, Premium, Corporate, or Tensor license (required for API access)

    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, teamviewer-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.

    Get TeamViewer prerequisites

    1. Sign in to the TeamViewer Management Consoleat https://login.teamviewer.com/ .
    2. Click on your user icon in the upper right corner and select Edit profile.
    3. Select Apps.
    4. Click Create script token.
    5. Provide the following configuration details:
      • Token name: Enter a descriptive name (for example, Google SecOps Integration ).
      • Permissions: Select the following permissions:
        • Account management > View account data
        • Session management > View session data
        • Connection reporting > View connection reports
    6. Click Create.

    7. Copy and save the generated script token in a secure location.

    8. Record your TeamViewer API Base URL: https://webapi.teamviewer.com/api/v1

    Verify permissions

    To verify the account has the required permissions:

    1. Sign in to the TeamViewer Management Console.
    2. Go to Edit profile > Apps.
    3. Locate your script token in the list.
    4. Verify that Connection reporting > View connection reportsis enabled.
    5. If this permission is not enabled, edit the token and add the required permission.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual script token 
 SCRIPT_TOKEN 
 = 
 "your-script-token" 
 API_BASE 
 = 
 "https://webapi.teamviewer.com/api/v1" 
 # Test API access 
curl  
-v  
-H  
 "Authorization: Bearer 
 ${ 
 SCRIPT_TOKEN 
 } 
 " 
  
 \ 
  
-H  
 "Accept: application/json" 
  
 \ 
  
 " 
 ${ 
 API_BASE 
 } 
 /reports/connections?from_date=2024-01-01T00:00:00Z&to_date=2024-01-01T01:00:00Z"

    If you receive a 200 response with JSON data, your credentials are configured correctly.

    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 teamviewer-collector-sa .
      • Service account description: Enter Service account for Cloud Run function to collect TeamViewer 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 your bucket name (for example, teamviewer-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, teamviewer-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 teamviewer-logs-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 TeamViewer 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 teamviewer-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 Pub/Sub topic ( teamviewer-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 ( teamviewer-collector-sa ).

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value
      GCS_BUCKET teamviewer-logs
      GCS_PREFIX teamviewer/audit/
      STATE_KEY teamviewer/audit/state.json
      WINDOW_SECONDS 3600
      HTTP_TIMEOUT 60
      MAX_RETRIES 3
      USER_AGENT teamviewer-to-gcs/1.0
      SCRIPT_TOKEN your-script-token (from TeamViewer prerequisites)
      API_BASE_URL https://webapi.teamviewer.com/api/v1

    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 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 
  
 urllib.request 
 import 
  
 urllib.parse 
 import 
  
 urllib.error 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 import 
  
 time 
 import 
  
 uuid 
 # Initialize Storage client 
 storage_client 
 = 
  storage 
 
 . 
  Client 
 
 () 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch TeamViewer audit logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 # Get environment variables 
 bucket_name 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 prefix 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'teamviewer/audit/' 
 ) 
 state_key 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'teamviewer/audit/state.json' 
 ) 
 window_sec 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'WINDOW_SECONDS' 
 , 
 '3600' 
 )) 
 http_timeout 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'HTTP_TIMEOUT' 
 , 
 '60' 
 )) 
 max_retries 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RETRIES' 
 , 
 '3' 
 )) 
 user_agent 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'USER_AGENT' 
 , 
 'teamviewer-to-gcs/1.0' 
 ) 
 # TeamViewer API credentials 
 api_base_url 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'API_BASE_URL' 
 ) 
 script_token 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SCRIPT_TOKEN' 
 ) 
 if 
 not 
 all 
 ([ 
 bucket_name 
 , 
 api_base_url 
 , 
 script_token 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 # Get GCS bucket 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 bucket_name 
 ) 
 # Load state (last processed timestamp) 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 state_key 
 ) 
 now 
 = 
 time 
 . 
 time 
 () 
 from_ts 
 = 
 float 
 ( 
  state 
 
 . 
 get 
 ( 
 'last_to_ts' 
 ) 
 or 
 ( 
 now 
 - 
 window_sec 
 )) 
 to_ts 
 = 
 now 
 print 
 ( 
 f 
 'Fetching TeamViewer audit data from 
 { 
 iso_format 
 ( 
 from_ts 
 ) 
 } 
 to 
 { 
 iso_format 
 ( 
 to_ts 
 ) 
 } 
 ' 
 ) 
 # Build audit API URL 
 url 
 = 
 build_audit_url 
 ( 
 api_base_url 
 , 
 from_ts 
 , 
 to_ts 
 ) 
 print 
 ( 
 f 
 'Fetching TeamViewer audit data from: 
 { 
 url 
 } 
 ' 
 ) 
 # Fetch audit data with retries and pagination 
 all_records 
 = 
 [] 
 offset_id 
 = 
 None 
 while 
 True 
 : 
 blob_data 
 , 
 content_type 
 , 
 next_offset 
 = 
 fetch_audit_data 
 ( 
 url 
 , 
 script_token 
 , 
 user_agent 
 , 
 http_timeout 
 , 
 max_retries 
 , 
 offset_id 
 ) 
 # Validate JSON data 
 try 
 : 
 audit_data 
 = 
 json 
 . 
 loads 
 ( 
 blob_data 
 ) 
 records 
 = 
 audit_data 
 . 
 get 
 ( 
 'records' 
 , 
 []) 
 all_records 
 . 
 extend 
 ( 
 records 
 ) 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 audit records (total: 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 )" 
 ) 
 # Check for pagination 
 if 
 next_offset 
 and 
 len 
 ( 
 records 
 ) 
 == 
 1000 
 : 
 offset_id 
 = 
 next_offset 
 print 
 ( 
 f 
 "Fetching next page with offset_id: 
 { 
 offset_id 
 } 
 " 
 ) 
 else 
 : 
 break 
 except 
 json 
 . 
 JSONDecodeError 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Invalid JSON received: 
 { 
 e 
 } 
 " 
 ) 
 break 
 if 
 all_records 
 : 
 # Write to GCS 
 key 
 = 
 put_audit_data 
 ( 
 bucket 
 , 
 prefix 
 , 
 json 
 . 
 dumps 
 ({ 
 'records' 
 : 
 all_records 
 }), 
 'application/json' 
 , 
 from_ts 
 , 
 to_ts 
 ) 
 print 
 ( 
 f 
 'Successfully wrote 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 audit records to 
 { 
 key 
 } 
 ' 
 ) 
 else 
 : 
 print 
 ( 
 'No audit records found' 
 ) 
 # Update state 
 state 
 [ 
 'last_to_ts' 
 ] 
 = 
 to_ts 
 state 
 [ 
 'last_successful_run' 
 ] 
 = 
 now 
 save_state 
 ( 
 bucket 
 , 
 state_key 
 , 
 state 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing TeamViewer 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: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 state 
 ): 
  
 """Save state to GCS.""" 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 , 
 separators 
 = 
 ( 
 ',' 
 , 
 ':' 
 )), 
 content_type 
 = 
 'application/json' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Warning: Could not save state: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 def 
  
 iso_format 
 ( 
 ts 
 ): 
  
 """Convert Unix timestamp to ISO 8601 format.""" 
 return 
 time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 T%H:%M:%SZ' 
 , 
 time 
 . 
 gmtime 
 ( 
 ts 
 )) 
 def 
  
 build_audit_url 
 ( 
 api_base_url 
 , 
 from_ts 
 , 
 to_ts 
 ): 
  
 """Build URL for TeamViewer audit API endpoint.""" 
 base_endpoint 
 = 
 f 
 " 
 { 
 api_base_url 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 } 
 /reports/connections" 
 params 
 = 
 { 
 'from_date' 
 : 
 iso_format 
 ( 
 from_ts 
 ), 
 'to_date' 
 : 
 iso_format 
 ( 
 to_ts 
 ) 
 } 
 query_string 
 = 
 urllib 
 . 
 parse 
 . 
 urlencode 
 ( 
 params 
 ) 
 return 
 f 
 " 
 { 
 base_endpoint 
 } 
 ? 
 { 
 query_string 
 } 
 " 
 def 
  
 fetch_audit_data 
 ( 
 url 
 , 
 script_token 
 , 
 user_agent 
 , 
 http_timeout 
 , 
 max_retries 
 , 
 offset_id 
 = 
 None 
 ): 
  
 """Fetch audit data from TeamViewer API with retries and pagination support.""" 
 # Add offset_id parameter if provided 
 if 
 offset_id 
 : 
 separator 
 = 
 '&' 
 if 
 '?' 
 in 
 url 
 else 
 '?' 
 url 
 = 
 f 
 " 
 { 
 url 
 }{ 
 separator 
 } 
 offset_id= 
 { 
 offset_id 
 } 
 " 
 attempt 
 = 
 0 
 while 
 True 
 : 
 req 
 = 
 urllib 
 . 
 request 
 . 
 Request 
 ( 
 url 
 , 
 method 
 = 
 'GET' 
 ) 
 req 
 . 
 add_header 
 ( 
 'User-Agent' 
 , 
 user_agent 
 ) 
 req 
 . 
 add_header 
 ( 
 'Authorization' 
 , 
 f 
 'Bearer 
 { 
 script_token 
 } 
 ' 
 ) 
 req 
 . 
 add_header 
 ( 
 'Accept' 
 , 
 'application/json' 
 ) 
 try 
 : 
 with 
 urllib 
 . 
 request 
 . 
 urlopen 
 ( 
 req 
 , 
 timeout 
 = 
 http_timeout 
 ) 
 as 
 r 
 : 
 response_data 
 = 
 r 
 . 
 read 
 () 
 content_type 
 = 
 r 
 . 
 headers 
 . 
 get 
 ( 
 'Content-Type' 
 ) 
 or 
 'application/json' 
 # Extract next_offset from response if present 
 try 
 : 
 data 
 = 
 json 
 . 
 loads 
 ( 
 response_data 
 ) 
 next_offset 
 = 
 data 
 . 
 get 
 ( 
 'next_offset' 
 ) 
 except 
 : 
 next_offset 
 = 
 None 
 return 
 response_data 
 , 
 content_type 
 , 
 next_offset 
 except 
 urllib 
 . 
 error 
 . 
 HTTPError 
 as 
 e 
 : 
 if 
 e 
 . 
 code 
 == 
 429 
 : 
 attempt 
 += 
 1 
 print 
 ( 
 f 
 'Rate limited (429) on attempt 
 { 
 attempt 
 } 
 ' 
 ) 
 if 
 attempt 
> max_retries 
 : 
 raise 
 time 
 . 
 sleep 
 ( 
 min 
 ( 
 60 
 , 
 2 
 ** 
 attempt 
 ) 
 + 
 ( 
 time 
 . 
 time 
 () 
 % 
 1 
 )) 
 else 
 : 
 print 
 ( 
 f 
 'HTTP error 
 { 
 e 
 . 
 code 
 } 
 : 
 { 
 e 
 . 
 reason 
 } 
 ' 
 ) 
 raise 
 except 
 urllib 
 . 
 error 
 . 
 URLError 
 as 
 e 
 : 
 attempt 
 += 
 1 
 print 
 ( 
 f 
 'URL error on attempt 
 { 
 attempt 
 } 
 : 
 { 
 e 
 } 
 ' 
 ) 
 if 
 attempt 
> max_retries 
 : 
 raise 
 time 
 . 
 sleep 
 ( 
 min 
 ( 
 60 
 , 
 2 
 ** 
 attempt 
 ) 
 + 
 ( 
 time 
 . 
 time 
 () 
 % 
 1 
 )) 
 def 
  
 put_audit_data 
 ( 
 bucket 
 , 
 prefix 
 , 
 blob_data 
 , 
 content_type 
 , 
 from_ts 
 , 
 to_ts 
 ): 
  
 """Write audit data to GCS.""" 
 ts_path 
 = 
 time 
 . 
 strftime 
 ( 
 '%Y/%m/ 
 %d 
 ' 
 , 
 time 
 . 
 gmtime 
 ( 
 to_ts 
 )) 
 uniq 
 = 
 f 
 " 
 { 
 int 
 ( 
 time 
 . 
 time 
 () 
  
 * 
  
 1e6 
 ) 
 } 
 _ 
 { 
 uuid 
 . 
 uuid4 
 () 
 . 
 hex 
 [: 
 8 
 ] 
 } 
 " 
 key 
 = 
 f 
 " 
 { 
 prefix 
 }{ 
 ts_path 
 } 
 /teamviewer_audit_ 
 { 
 int 
 ( 
 from_ts 
 ) 
 } 
 _ 
 { 
 int 
 ( 
 to_ts 
 ) 
 } 
 _ 
 { 
 uniq 
 } 
 .json" 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  metadata 
 
 = 
 { 
 'source' 
 : 
 'teamviewer-audit' 
 , 
 'from_timestamp' 
 : 
 str 
 ( 
 int 
 ( 
 from_ts 
 )), 
 'to_timestamp' 
 : 
 str 
 ( 
 int 
 ( 
 to_ts 
 )) 
 } 
 blob 
 . 
  upload_from_string 
 
 ( 
 blob_data 
 , 
 content_type 
 = 
 content_type 
 ) 
 return 
 key
      • Second file: requirements.txt:
        functions 
 - 
 framework 
 == 
 3 
 .* 
 google 
 - 
 cloud 
 - 
 storage 
 == 
 2 
 .*

    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 teamviewer-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 Pub/Sub topic ( teamviewer-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 ( teamviewer-logs-collector-hourly ).
    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 ( teamviewer-logs-collector ).
    6. Click the Logstab.

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

       Fetching TeamViewer audit data from YYYY-MM-DDTHH:MM:SSZ to YYYY-MM-DDTHH:MM:SSZ
Retrieved X audit records (total: X)
Successfully wrote X audit records to teamviewer/audit/YYYY/MM/DD/teamviewer_audit_...json

    8. Go to Cloud Storage > Buckets.

    9. Click your bucket name ( teamviewer-logs ).

    10. Navigate to the prefix folder ( teamviewer/audit/ ).

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

    If you see errors in the logs:

    • HTTP 401: Check the SCRIPT_TOKEN environment variable matches your TeamViewer script token
    • HTTP 403: Verify the script token has Connection reporting > View connection reportspermission
    • HTTP 429: Rate limiting - function will automatically retry with exponential backoff
    • Missing environment variables: Check all required variables ( GCS_BUCKET , API_BASE_URL , SCRIPT_TOKEN ) are set

    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, TeamViewer logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select TeamVieweras 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 ( teamviewer-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 TeamViewer 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, TeamViewer logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select TeamVieweras 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://teamviewer-logs/teamviewer/audit/

        • Replace:

          • teamviewer-logs : Your GCS bucket name.
          • teamviewer/audit/ : Prefix/folder 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
    AffectedItem
    		 metadata.product_log_id The value of AffectedItem from the raw log is directly mapped to this UDM field.
    EventDetails.NewValue
    		 principal.resource.attribute.labels.value If PropertyName contains (server), the NewValue is used as the value of a label in principal.resource.attribute.labels.
    EventDetails.NewValue
    		 principal.user.user_display_name If PropertyName is Name of participant, the NewValue is used as the user display name for the principal.
    EventDetails.NewValue
    		 principal.user.userid If PropertyName is ID of participant, the NewValue is used as the user ID for the principal.
    EventDetails.NewValue
    		 security_result.about.labels.value For all other PropertyName values (except those handled by specific conditions), the NewValue is used as the value of a label within the security_result.about.labels array.
    EventDetails.NewValue
    		 target.file.full_path If PropertyName is Source file, the NewValue is used as the full path for the target file.
    EventDetails.NewValue
    		 target.resource.attribute.labels.value If PropertyName contains (client), the NewValue is used as the value of a label in target.resource.attribute.labels.
    EventDetails.NewValue
    		 target.user.user_display_name If PropertyName is Name of presenter, the NewValue is parsed. If it's an integer, it's discarded. Otherwise, it's used as the user display name for the target.
    EventDetails.NewValue
    		 target.user.userid If PropertyName is ID of presenter, the NewValue is used as the user ID for the target.
    EventDetails.PropertyName
    		 principal.resource.attribute.labels.key If PropertyName contains (server), the PropertyName is used as the key of a label in principal.resource.attribute.labels.
    EventDetails.PropertyName
    		 security_result.about.labels.key For all other PropertyName values (except those handled by specific conditions), the PropertyName is used as the key of a label within the security_result.about.labels array.
    EventDetails.PropertyName
    		 target.resource.attribute.labels.key If PropertyName contains (client), the PropertyName is used as the key of a label in target.resource.attribute.labels.
    EventName
    		 metadata.product_event_type The value of EventName from the raw log is directly mapped to this UDM field.
    Timestamp
    		 metadata.event_timestamp The value of Timestamp from the raw log is parsed and used as the event timestamp in the metadata.
    metadata.event_type
    		 Set to USER_UNCATEGORIZED if src_user (derived from ID of participant) is not empty, otherwise set to USER_RESOURCE_ACCESS.
    metadata.vendor_name
    		 Hardcoded to TEAMVIEWER.
    metadata.product_name
    		 Hardcoded to TEAMVIEWER.
    network.application_protocol
    		 Hardcoded to TEAMVIEWER.

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