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    Collect Zendesk CRM logs

    Supported in:

    This document explains how to ingest Zendesk Customer Relationship Management (CRM) logs to Google Security Operations using Google Cloud Storage. Zendesk CRM provides customer support and ticketing management capabilities. The platform tracks customer interactions, support tickets, and administrative activities through audit logs and ticket data.

    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 Zendesk (Admin role required for API token creation)
    • Zendesk Enterprise plan (required for Audit Logs API access)

    Get Zendesk prerequisites

    Confirm plan and role

    You must be a Zendesk Admin to create API tokens or OAuth clients. The Audit Logs API is available only on Enterprise plan and returns a maximum of 100 records per page. If your account isn't Enterprise, you can still collect incremental tickets data.

    Turn on API token access (one-time)

    1. In the Admin Center, go to Apps and integrations > APIs > Zendesk API.
    2. In the Settingstab, enable Token Access.

    Generate an API token (for Basic auth)

    1. Go to Apps and integrations > APIs > Zendesk API.
    2. Click the Add API tokenbutton.
    3. Optionally add a API token description.
    4. Click Create.
    5. Copy and save the API token now (you won't be able to view it again).

    6. Save the admin email that will authenticate with this token.

    (Optional) Create an OAuth client (for Bearer auth instead of API token)

    1. Go to Apps and integrations > APIs > Zendesk API.
    2. Click the OAuth Clientstab.
    3. Click Add OAuth client.
    4. Fill in the Client Name, Unique Identifier(auto), Redirect URLs(can be placeholder if you only mint tokens with API).
    5. Click Save.
    6. Create an access token for the integration and grant the minimum scopes required by this guide:
      • tickets:read (for Incremental Tickets)
      • auditlogs:read (for Audit Logs; Enterprise only)
    7. Copy the access token (paste into ZENDESK_BEARER_TOKEN environment variable) and record the client ID/secret securely (for future token refresh flows).

    Record your Zendesk base URL

    Use https://<your_subdomain>.zendesk.com (paste into ZENDESK_BASE_URL environment variable).

    What to save for later

    • Base URL (for example, https://acme.zendesk.com )
    • Email Address of the administrator user (for API token auth)
    • API Token (if using AUTH_MODE=token ) or OAuth access token (if using AUTH_MODE=bearer )
    • (Optional): OAuth client id/secret for lifecycle management

    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, zendesk-crm-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 zendesk-crm-collector-sa .
      • Service account description: Enter Service account for Cloud Run function to collect Zendesk CRM 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.
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email (for example, zendesk-crm-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 zendesk-crm-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 Zendesk 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 zendesk-crm-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 zendesk-crm-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 zendesk-crm-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
      		zendesk-crm-logs GCS bucket name
      GCS_PREFIX
      		zendesk/crm/ Prefix for log files
      STATE_KEY
      		zendesk/crm/state.json State file path
      ZENDESK_BASE_URL
      		https://your_subdomain.zendesk.com Zendesk base URL
      AUTH_MODE
      		token Authentication mode ( token or bearer )
      ZENDESK_EMAIL
      		analyst@example.com Admin email for API token auth
      ZENDESK_API_TOKEN
      		<api_token> API token for authentication
      ZENDESK_BEARER_TOKEN
      		<leave empty unless using OAuth bearer> OAuth bearer token (optional)
      RESOURCES
      		audit_logs,incremental_tickets Resources to collect
      MAX_PAGES
      		20 Maximum pages per run
      LOOKBACK_SECONDS
      		3600 Initial lookback period
      HTTP_TIMEOUT
      		60 HTTP request timeout
      HTTP_RETRIES
      		3 HTTP retry attempts

    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 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 import 
  
 base64 
 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 
 
 () 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch logs from Zendesk API 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' 
 , 
 'zendesk/crm/' 
 ) 
 state_key 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'zendesk/crm/state.json' 
 ) 
 base_url 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZENDESK_BASE_URL' 
 , 
 '' 
 ) 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 auth_mode 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'AUTH_MODE' 
 , 
 'token' 
 ) 
 . 
 lower 
 () 
 email 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZENDESK_EMAIL' 
 , 
 '' 
 ) 
 api_token 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZENDESK_API_TOKEN' 
 , 
 '' 
 ) 
 bearer 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'ZENDESK_BEARER_TOKEN' 
 , 
 '' 
 ) 
 resources 
 = 
 [ 
 r 
 . 
 strip 
 () 
 for 
 r 
 in 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'RESOURCES' 
 , 
 'audit_logs,incremental_tickets' 
 ) 
 . 
 split 
 ( 
 ',' 
 ) 
 if 
 r 
 . 
 strip 
 ()] 
 max_pages 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_PAGES' 
 , 
 '20' 
 )) 
 lookback 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_SECONDS' 
 , 
 '3600' 
 )) 
 http_timeout 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'HTTP_TIMEOUT' 
 , 
 '60' 
 )) 
 http_retries 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'HTTP_RETRIES' 
 , 
 '3' 
 )) 
 if 
 not 
 all 
 ([ 
 bucket_name 
 , 
 base_url 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 # Get GCS bucket 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 bucket_name 
 ) 
 # Load state 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 state_key 
 ) 
 print 
 ( 
 f 
 'Processing resources: 
 { 
 resources 
 } 
 ' 
 ) 
 summary 
 = 
 [] 
 if 
 'audit_logs' 
 in 
 resources 
 : 
 res 
 = 
 fetch_audit_logs 
 ( 
 bucket 
 , 
 prefix 
 , 
  state 
 
 . 
 get 
 ( 
 'audit_logs' 
 , 
 {}), 
 base_url 
 , 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 , 
 max_pages 
 , 
 http_timeout 
 , 
 http_retries 
 ) 
 state 
 [ 
 'audit_logs' 
 ] 
 = 
 { 
 'next_url' 
 : 
 res 
 . 
 get 
 ( 
 'next_url' 
 )} 
 summary 
 . 
 append 
 ( 
 res 
 ) 
 if 
 'incremental_tickets' 
 in 
 resources 
 : 
 res 
 = 
 fetch_incremental_tickets 
 ( 
 bucket 
 , 
 prefix 
 , 
  state 
 
 . 
 get 
 ( 
 'incremental_tickets' 
 , 
 {}), 
 base_url 
 , 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 , 
 max_pages 
 , 
 lookback 
 , 
 http_timeout 
 , 
 http_retries 
 ) 
 state 
 [ 
 'incremental_tickets' 
 ] 
 = 
 { 
 'cursor' 
 : 
 res 
 . 
 get 
 ( 
 'cursor' 
 )} 
 summary 
 . 
 append 
 ( 
 res 
 ) 
 # Save state 
 save_state 
 ( 
 bucket 
 , 
 state_key 
 , 
 state 
 ) 
 print 
 ( 
 f 
 'Successfully processed logs: 
 { 
 summary 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 get_headers 
 ( 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 ): 
  
 """Get authentication headers.""" 
 if 
 auth_mode 
 == 
 'bearer' 
 and 
 bearer 
 : 
 return 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 bearer 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 } 
 if 
 auth_mode 
 == 
 'token' 
 and 
 email 
 and 
 api_token 
 : 
 auth_string 
 = 
 f 
 ' 
 { 
 email 
 } 
 /token: 
 { 
 api_token 
 } 
 ' 
 auth_bytes 
 = 
 auth_string 
 . 
 encode 
 ( 
 'utf-8' 
 ) 
 token 
 = 
 base64 
 . 
 b64encode 
 ( 
 auth_bytes 
 ) 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 return 
 { 
 'Authorization' 
 : 
 f 
 'Basic 
 { 
 token 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 } 
 raise 
 RuntimeError 
 ( 
 'Invalid auth settings: provide token (EMAIL + API_TOKEN) or BEARER' 
 ) 
 def 
  
 http_get_json 
 ( 
 url 
 , 
 headers 
 , 
 timeout 
 , 
 retries 
 ): 
  
 """Make HTTP GET request with retries and exponential backoff.""" 
 attempt 
 = 
 0 
 backoff 
 = 
 1.0 
 while 
 True 
 : 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 headers 
 = 
 headers 
 , 
 timeout 
 = 
 timeout 
 ) 
 if 
 response 
 . 
 status 
 == 
 200 
 : 
 return 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 elif 
 response 
 . 
 status 
 in 
 ( 
 429 
 , 
 500 
 , 
 502 
 , 
 503 
 , 
 504 
 ) 
 and 
 attempt 
< retries 
 : 
 retry_after 
 = 
 int 
 ( 
 response 
 . 
 headers 
 . 
 get 
 ( 
 'Retry-After' 
 , 
 int 
 ( 
 backoff 
 ))) 
 print 
 ( 
 f 
 'HTTP 
 { 
 response 
 . 
 status 
 } 
 : Retrying after 
 { 
 retry_after 
 } 
 s (attempt 
 { 
 attempt 
  
 + 
  
 1 
 } 
 / 
 { 
 retries 
 } 
 )' 
 ) 
 time 
 . 
 sleep 
 ( 
 max 
 ( 
 1 
 , 
 retry_after 
 )) 
 backoff 
 = 
 min 
 ( 
 backoff 
 * 
 2 
 , 
 30.0 
 ) 
 attempt 
 += 
 1 
 continue 
 else 
 : 
 raise 
 Exception 
 ( 
 f 
 'HTTP 
 { 
 response 
 . 
 status 
 } 
 : 
 { 
 response 
 . 
 data 
 . 
 decode 
 ( 
 "utf-8" 
 ) 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 if 
 attempt 
< retries 
 : 
 print 
 ( 
 f 
 'Request error: 
 { 
 e 
 } 
 . Retrying after 
 { 
 int 
 ( 
 backoff 
 ) 
 } 
 s (attempt 
 { 
 attempt 
  
 + 
  
 1 
 } 
 / 
 { 
 retries 
 } 
 )' 
 ) 
 time 
 . 
 sleep 
 ( 
 backoff 
 ) 
 backoff 
 = 
 min 
 ( 
 backoff 
 * 
 2 
 , 
 30.0 
 ) 
 attempt 
 += 
 1 
 continue 
 raise 
 def 
  
 put_page 
 ( 
 bucket 
 , 
 prefix 
 , 
 payload 
 , 
 resource 
 ): 
  
 """Write page to GCS.""" 
 ts 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 key 
 = 
 f 
 ' 
 { 
 prefix 
 }{ 
 ts 
 . 
 strftime 
 ( 
 "%Y/%m/ 
 %d 
 /%H%M%S" 
 ) 
 } 
 -zendesk- 
 { 
 resource 
 } 
 .json' 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 payload 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 return 
 key 
 def 
  
 fetch_audit_logs 
 ( 
 bucket 
 , 
 prefix 
 , 
 state 
 , 
 base_url 
 , 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 , 
 max_pages 
 , 
 timeout 
 , 
 retries 
 ): 
  
 """Fetch audit logs with pagination.""" 
 headers 
 = 
 get_headers 
 ( 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 ) 
 next_url 
 = 
  state 
 
 . 
 get 
 ( 
 'next_url' 
 ) 
 or 
 f 
 ' 
 { 
 base_url 
 } 
 /api/v2/audit_logs.json' 
 pages 
 = 
 0 
 written 
 = 
 0 
 last_next 
 = 
 None 
 while 
 pages 
< max_pages 
 and 
 next_url 
 : 
 data 
 = 
 http_get_json 
 ( 
 next_url 
 , 
 headers 
 , 
 timeout 
 , 
 retries 
 ) 
 put_page 
 ( 
 bucket 
 , 
 prefix 
 , 
 data 
 , 
 'audit_logs' 
 ) 
 written 
 += 
 len 
 ( 
 data 
 . 
 get 
 ( 
 'audit_logs' 
 , 
 [])) 
 # Use next_page for pagination 
 last_next 
 = 
 data 
 . 
 get 
 ( 
 'next_page' 
 ) 
 next_url 
 = 
 last_next 
 pages 
 += 
 1 
 print 
 ( 
 f 
 'Audit logs page 
 { 
 pages 
 } 
 : Retrieved 
 { 
 len 
 ( 
 data 
 . 
 get 
 ( 
 "audit_logs" 
 , 
  
 [])) 
 } 
 records' 
 ) 
 return 
 { 
 'resource' 
 : 
 'audit_logs' 
 , 
 'pages' 
 : 
 pages 
 , 
 'written' 
 : 
 written 
 , 
 'next_url' 
 : 
 last_next 
 } 
 def 
  
 fetch_incremental_tickets 
 ( 
 bucket 
 , 
 prefix 
 , 
 state 
 , 
 base_url 
 , 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 , 
 max_pages 
 , 
 lookback 
 , 
 timeout 
 , 
 retries 
 ): 
  
 """Fetch incremental tickets with cursor-based pagination.""" 
 headers 
 = 
 get_headers 
 ( 
 auth_mode 
 , 
 email 
 , 
 api_token 
 , 
 bearer 
 ) 
 cursor 
 = 
  state 
 
 . 
 get 
 ( 
 'cursor' 
 ) 
 if 
 not 
 cursor 
 : 
 start 
 = 
 int 
 ( 
 time 
 . 
 time 
 ()) 
 - 
 lookback 
 next_url 
 = 
 f 
 ' 
 { 
 base_url 
 } 
 /api/v2/incremental/tickets/cursor.json?start_time= 
 { 
 start 
 } 
 ' 
 else 
 : 
 next_url 
 = 
 f 
 ' 
 { 
 base_url 
 } 
 /api/v2/incremental/tickets/cursor.json?cursor= 
 { 
 cursor 
 } 
 ' 
 pages 
 = 
 0 
 written 
 = 
 0 
 last_cursor 
 = 
 None 
 while 
 pages 
< max_pages 
 and 
 next_url 
 : 
 data 
 = 
 http_get_json 
 ( 
 next_url 
 , 
 headers 
 , 
 timeout 
 , 
 retries 
 ) 
 put_page 
 ( 
 bucket 
 , 
 prefix 
 , 
 data 
 , 
 'incremental_tickets' 
 ) 
 written 
 += 
 len 
 ( 
 data 
 . 
 get 
 ( 
 'tickets' 
 , 
 [])) 
 # Extract cursor from after_cursor field 
 last_cursor 
 = 
 data 
 . 
 get 
 ( 
 'after_cursor' 
 ) 
 if 
 last_cursor 
 : 
 next_url 
 = 
 f 
 ' 
 { 
 base_url 
 } 
 /api/v2/incremental/tickets/cursor.json?cursor= 
 { 
 last_cursor 
 } 
 ' 
 else 
 : 
 next_url 
 = 
 None 
 pages 
 += 
 1 
 print 
 ( 
 f 
 'Incremental tickets page 
 { 
 pages 
 } 
 : Retrieved 
 { 
 len 
 ( 
 data 
 . 
 get 
 ( 
 "tickets" 
 , 
  
 [])) 
 } 
 records' 
 ) 
 return 
 { 
 'resource' 
 : 
 'incremental_tickets' 
 , 
 'pages' 
 : 
 pages 
 , 
 'written' 
 : 
 written 
 , 
 'cursor' 
 : 
 last_cursor 
 } 
 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 
 { 
 'audit_logs' 
 : 
 {}, 
 'incremental_tickets' 
 : 
 {}} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 state 
 ): 
  
 """Save state to GCS.""" 
 try 
 : 
 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 
 ) 
 } 
 ' 
 )
      • 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 zendesk-crm-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 zendesk-crm-trigger
      Message body {} (empty JSON object)

    4. Click Create.

    Schedule frequency options

    • Choose frequency based on log volume and latency requirements:

      Frequency Cron Expression Use Case
      Every 5 minutes
      		 */5 * * * * High-volume, low-latency
      Every 15 minutes
      		 */15 * * * * Medium volume
      Every hour
      		 0 * * * * Standard (recommended)
      Every 6 hours
      		 0 */6 * * * Low volume, batch processing
      Daily
      		 0 0 * * * Historical data collection

    Test the integration

    1. In the Cloud Schedulerconsole, find your job.
    2. Click Force runto trigger the job manually.
    3. Wait a few seconds.
    4. Go to Cloud Run > Services.
    5. Click your function name zendesk-crm-collector .
    6. Click the Logstab.

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

        Proc 
 essing 
  
 resources 
 : 
  
 [ 
 'audit_logs' 
 , 
  
 'incremental_tickets' 
 ] 
 Audit 
  
 logs 
  
 page 
  
 1 
 : 
  
 Retrieved 
  
 X 
  
 records 
 Incremental 
  
 tickets 
  
 page 
  
 1 
 : 
  
 Retrieved 
  
 X 
  
 records 
 Successfully 
  
 processed 
  
 logs 
 : 
  
 [ 
 ... 
 ]

    8. Go to Cloud Storage > Buckets.

    9. Click your bucket name.

    10. Navigate to the prefix folder zendesk/crm/ .

    11. Verify that new .json files were created with the current timestamp.

    If you see errors in the logs:

    • HTTP 401: Check API credentials in environment variables
    • HTTP 403: Verify account has required permissions (Admin role, Enterprise plan for audit logs)
    • HTTP 429: Rate limiting - function will automatically retry with exponential backoff
    • Missing environment variables: Check all required variables 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, Zendesk CRM logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Zendesk CRMas 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.
    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 Zendesk CRM 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, Zendesk CRM logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Zendesk CRMas 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://zendesk-crm-logs/zendesk/crm/

        • Replace:

          • zendesk-crm-logs : Your GCS bucket name.
          • zendesk/crm/ : 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.

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

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