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    Collect Rippling activity logs

    Supported in:

    This document explains how to ingest Rippling activity logs to Google Security Operations using Google Cloud Storage. Rippling is a workforce management platform that provides HR, IT, and Finance solutions including payroll, benefits, employee onboarding, device management, and application provisioning. The Company Activity API provides audit logs of administrative and user actions across the Rippling platform.

    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 Rippling (API token with access to Company Activity)

    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, rippling-activity-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.

    Collect Rippling API credentials

    1. Sign in to Rippling Admin.
    2. Go to Search > API Tokens.
      • Alternative path: Settings > Company Settings > API Tokens.
    3. Click Create API token.
    4. Provide the following configuration details:
      • Name: Enter a unique and meaningful name (for example, Google SecOps GCS Export ).
      • API version: Select Base API (v1).
      • Scopes/Permissions: Enable company:activity:read(required for Company Activity).
    5. Click Create.

    6. Copy and save the token value in a secure location. You will use it as a bearer token.

    The Cloud Run function needs a service account with permissions to write to GCS bucket.

    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 rippling-logs-collector-sa .
      • Service account description: Enter Service account for Cloud Run function to collect Rippling activity logs .
    4. Click Create and Continue.
    5. In the Grant this service account access to projectsection:
      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, rippling-logs-collector-sa@your-project.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 rippling-activity-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 Rippling Company Activity 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 rippling-activity-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 rippling-activity-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 rippling-logs-collector-sa .

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value
      GCS_BUCKET rippling-activity-logs
      GCS_PREFIX rippling/activity/
      STATE_KEY rippling/activity/state.json
      RIPPLING_API_TOKEN your-api-token
      RIPPLING_ACTIVITY_URL https://api.rippling.com/platform/api/company_activity
      LIMIT 1000
      MAX_PAGES 10
      LOOKBACK_MINUTES 60
      END_LAG_SECONDS 120

    10. Scroll down in the Variables & Secretstab to Requests:

      • Request timeout: Enter 600 seconds (10 minutes).

    11. Go to the Settingstab in Containers:

      • In the Resourcessection:
        • Memory: Select 512 MiBor higher.
        • CPU: Select 1.
      • Click Done.

    12. Scroll to Execution environment:

      • Select Default(recommended).

    13. In the Revision scalingsection:

      • Minimum number of instances: Enter 0 .
      • Maximum number of instances: Enter 100 (or adjust based on expected load).

    14. Click Create.

    15. Wait for the service to be created (1-2 minutes).

    16. 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 
 , 
 timedelta 
 import 
  
 time 
 # Initialize HTTP client 
 http 
 = 
 urllib3 
 . 
 PoolManager 
 ( 
 timeout 
 = 
 urllib3 
 . 
 Timeout 
 ( 
 connect 
 = 
 5.0 
 , 
 read 
 = 
 60.0 
 ), 
 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 Rippling Company Activity 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' 
 , 
 'rippling/activity/' 
 ) 
 state_key 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'rippling/activity/state.json' 
 ) 
 # Rippling API configuration 
 api_token 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'RIPPLING_API_TOKEN' 
 ) 
 activity_url 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'RIPPLING_ACTIVITY_URL' 
 , 
 'https://api.rippling.com/platform/api/company_activity' 
 ) 
 limit 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LIMIT' 
 , 
 '1000' 
 )) 
 max_pages 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_PAGES' 
 , 
 '10' 
 )) 
 lookback_minutes 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_MINUTES' 
 , 
 '60' 
 )) 
 end_lag_seconds 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'END_LAG_SECONDS' 
 , 
 '120' 
 )) 
 if 
 not 
 all 
 ([ 
 bucket_name 
 , 
 api_token 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 # Get GCS bucket 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 bucket_name 
 ) 
 # Load state (last processed timestamp and cursor) 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 state_key 
 ) 
 since_iso 
 = 
  state 
 
 . 
 get 
 ( 
 'since' 
 ) 
 next_cursor 
 = 
  state 
 
 . 
 get 
 ( 
 'next' 
 ) 
 # Calculate time window 
 run_end 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 - 
 timedelta 
 ( 
 seconds 
 = 
 end_lag_seconds 
 ) 
 end_iso 
 = 
 run_end 
 . 
 replace 
 ( 
 microsecond 
 = 
 0 
 ) 
 . 
 isoformat 
 () 
 . 
 replace 
 ( 
 '+00:00' 
 , 
 'Z' 
 ) 
 if 
 since_iso 
 is 
 None 
 : 
 since_iso 
 = 
 iso_from_epoch 
 ( 
 time 
 . 
 time 
 () 
 - 
 lookback_minutes 
 * 
 60 
 ) 
 else 
 : 
 try 
 : 
 since_iso 
 = 
 ( 
 parse_iso 
 ( 
 since_iso 
 ) 
 + 
 timedelta 
 ( 
 seconds 
 = 
 1 
 )) 
 . 
 replace 
 ( 
 microsecond 
 = 
 0 
 ) 
 . 
 isoformat 
 () 
 . 
 replace 
 ( 
 '+00:00' 
 , 
 'Z' 
 ) 
 except 
 Exception 
 : 
 since_iso 
 = 
 iso_from_epoch 
 ( 
 time 
 . 
 time 
 () 
 - 
 lookback_minutes 
 * 
 60 
 ) 
 print 
 ( 
 f 
 'Processing logs from 
 { 
 since_iso 
 } 
 to 
 { 
 end_iso 
 } 
 ' 
 ) 
 run_ts_iso 
 = 
 end_iso 
 pages 
 = 
 0 
 total 
 = 
 0 
 newest_ts 
 = 
 None 
 pending_next 
 = 
 None 
 # Fetch logs with pagination 
 while 
 pages 
< max_pages 
 : 
 params 
 = 
 { 
 'limit' 
 : 
 str 
 ( 
 limit 
 )} 
 if 
 next_cursor 
 : 
 params 
 [ 
 'next' 
 ] 
 = 
 next_cursor 
 else 
 : 
 params 
 [ 
 'startDate' 
 ] 
 = 
 since_iso 
 params 
 [ 
 'endDate' 
 ] 
 = 
 end_iso 
 # Build URL with query parameters 
 url 
 = 
 build_url 
 ( 
 activity_url 
 , 
 params 
 ) 
 # Fetch data from Rippling API 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 api_token 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 } 
 # Implement exponential backoff for rate limiting 
 backoff 
 = 
 1.0 
 max_retries 
 = 
 3 
 retry_count 
 = 
 0 
 while 
 retry_count 
< max_retries 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 headers 
 = 
 headers 
 , 
 timeout 
 = 
 60.0 
 ) 
 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 
 ) 
 retry_count 
 += 
 1 
 continue 
 break 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 'Error: API returned status 
 { 
 response 
 . 
 status 
 } 
 ' 
 ) 
 break 
 data 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 # Write page to GCS 
 write_to_gcs 
 ( 
 bucket 
 , 
 prefix 
 , 
 data 
 , 
 run_ts_iso 
 , 
 pages 
 ) 
 # Extract events 
 events 
 = 
 data 
 . 
 get 
 ( 
 'events' 
 ) 
 or 
 [] 
 total 
 += 
 len 
 ( 
 events 
 ) 
 if 
 isinstance 
 ( 
 events 
 , 
 list 
 ) 
 else 
 0 
 # Track newest timestamp 
 if 
 isinstance 
 ( 
 events 
 , 
 list 
 ): 
 for 
 ev 
 in 
 events 
 : 
 t 
 = 
 ev 
 . 
 get 
 ( 
 'timestamp' 
 ) 
 or 
 ev 
 . 
 get 
 ( 
 'time' 
 ) 
 or 
 ev 
 . 
 get 
 ( 
 'event_time' 
 ) 
 if 
 isinstance 
 ( 
 t 
 , 
 str 
 ): 
 try 
 : 
 dt_ts 
 = 
 parse_iso 
 ( 
 t 
 ) 
 if 
 newest_ts 
 is 
 None 
 or 
 dt_ts 
> newest_ts 
 : 
 newest_ts 
 = 
 dt_ts 
 except 
 Exception 
 : 
 pass 
 # Check for next page 
 nxt 
 = 
 data 
 . 
 get 
 ( 
 'next' 
 ) 
 pages 
 += 
 1 
 if 
 nxt 
 : 
 next_cursor 
 = 
 nxt 
 pending_next 
 = 
 nxt 
 continue 
 else 
 : 
 pending_next 
 = 
 None 
 break 
 # Update state 
 new_since_iso 
 = 
 ( 
 newest_ts 
 or 
 run_end 
 ) 
 . 
 replace 
 ( 
 microsecond 
 = 
 0 
 ) 
 . 
 isoformat 
 () 
 . 
 replace 
 ( 
 '+00:00' 
 , 
 'Z' 
 ) 
 save_state 
 ( 
 bucket 
 , 
 state_key 
 , 
 { 
 'since' 
 : 
 new_since_iso 
 , 
 'next' 
 : 
 pending_next 
 }) 
 print 
 ( 
 f 
 'Successfully processed 
 { 
 total 
 } 
 events across 
 { 
 pages 
 } 
 pages' 
 ) 
 print 
 ( 
 f 
 'Updated state: since= 
 { 
 new_since_iso 
 } 
 , next= 
 { 
 pending_next 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing 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 
  
 write_to_gcs 
 ( 
 bucket 
 , 
 prefix 
 , 
 payload 
 , 
 run_ts_iso 
 , 
 page_index 
 ): 
  
 """Write payload to GCS.""" 
 try 
 : 
 day_path 
 = 
 parse_iso 
 ( 
 run_ts_iso 
 ) 
 . 
 strftime 
 ( 
 '%Y/%m/ 
 %d 
 ' 
 ) 
 key 
 = 
 f 
 " 
 { 
 prefix 
 . 
 strip 
 ( 
 '/' 
 ) 
 } 
 / 
 { 
 day_path 
 } 
 / 
 { 
 run_ts_iso 
 . 
 replace 
 ( 
 ':' 
 , 
  
 '' 
 ) 
 . 
 replace 
 ( 
 '-' 
 , 
  
 '' 
 ) 
 } 
 -page 
 { 
 page_index 
 : 
 05d 
 } 
 -company_activity.json" 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 payload 
 , 
 separators 
 = 
 ( 
 ',' 
 , 
 ':' 
 )), 
 content_type 
 = 
 'application/json' 
 ) 
 print 
 ( 
 f 
 'Wrote page 
 { 
 page_index 
 } 
 to 
 { 
 key 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error writing to GCS: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 parse_iso 
 ( 
 ts 
 ): 
  
 """Parse ISO 8601 timestamp.""" 
 if 
 ts 
 . 
 endswith 
 ( 
 'Z' 
 ): 
 ts 
 = 
 ts 
 [: 
 - 
 1 
 ] 
 + 
 '+00:00' 
 return 
 datetime 
 . 
 fromisoformat 
 ( 
 ts 
 ) 
 def 
  
 iso_from_epoch 
 ( 
 sec 
 ): 
  
 """Convert epoch seconds to ISO 8601 timestamp.""" 
 return 
 datetime 
 . 
 fromtimestamp 
 ( 
 sec 
 , 
 tz 
 = 
 timezone 
 . 
 utc 
 ) 
 . 
 replace 
 ( 
 microsecond 
 = 
 0 
 ) 
 . 
 isoformat 
 () 
 . 
 replace 
 ( 
 '+00:00' 
 , 
 'Z' 
 ) 
 def 
  
 build_url 
 ( 
 base 
 , 
 params 
 ): 
  
 """Build URL with query parameters.""" 
 if 
 not 
 params 
 : 
 return 
 base 
 query_string 
 = 
 '&' 
 . 
 join 
 ([ 
 f 
 ' 
 { 
 k 
 } 
 = 
 { 
 v 
 } 
 ' 
 for 
 k 
 , 
 v 
 in 
 params 
 . 
 items 
 ()]) 
 return 
 f 
 ' 
 { 
 base 
 } 
 ? 
 { 
 query_string 
 } 
 '
      • 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 rippling-activity-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 rippling-activity-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 scheduler job

    1. In the Cloud Schedulerconsole, find your job.
    2. Click Force runto trigger manually.
    3. Wait a few seconds and go to Cloud Run > Services > rippling-activity-collector > Logs.
    4. Verify the function executed successfully.
    5. Check the GCS bucket to confirm logs were written.

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

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

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

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

    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 Rippling Activity 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, Rippling Activity Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Rippling Activity 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://rippling-activity-logs/rippling/activity/

        • Replace:

          • rippling-activity-logs : Your GCS bucket name.
          • rippling/activity/ : Prefix/folder path where logs are stored (must match GCS_PREFIX environment variable).

      • 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 (for example, rippling.activity ).

      • Ingestion labels: Optional 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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