Collect Rippling activity logs

Supported in:

Google secops SIEM

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

Go to the Google Cloud Console.
Select your project or create a new one.
In the navigation menu, go to Cloud Storage > Buckets.
Click Create bucket.

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

Click Create.

Collect Rippling API credentials

Sign in to Rippling Admin.
Go to Search > API Tokens.
- Alternative path: Settings > Company Settings > API Tokens.
Click Create API token.
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).
Click Create.
Copy and save the token value in a secure location. You will use it as a bearer token.

Note: The API token will only be displayed once. Store it securely.

Create service account for Cloud Run function

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

Create service account

In the GCP Console, go to IAM & Admin > Service Accounts.
Click Create Service Account.
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.
Click Create and Continue.
In the Grant this service account access to project section:
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.
Click Continue.
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:

Go to Cloud Storage > Buckets.
Click your bucket name.
Go to the Permissions tab.
Click Grant access.
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.
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.

In the GCP Console, go to Pub/Sub > Topics.
Click Create topic.
Provide the following configuration details:
- Topic ID: Enter rippling-activity-trigger.
- Leave other settings as default.
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.

In the GCP Console, go to Cloud Run.
Click Create service.
Select Function (use an inline editor to create a function).
In the Configure section, 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 3.12 or later
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.
In the Authentication section:
1. Select Require authentication.
2. Check Identity and Access Management (IAM).
Note: Pub/Sub will automatically handle authentication when invoking the function.
Scroll down and expand Containers, Networking, Security.
Go to the Security tab:
- Service account: Select the service account rippling-logs-collector-sa.

Setting	Value
Service name	`rippling-activity-collector`
Region	Select region matching your GCS bucket (for example, `us-central1`)
Runtime	Select Python 3.12 or later

Go to the Containers tab:

Click Variables & Secrets.
Click + Add variable for 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`

Scroll down in the Variables & Secrets tab to Requests:
- Request timeout: Enter 600 seconds (10 minutes).
Go to the Settings tab in Containers:
- In the Resources section:
  - Memory: Select 512 MiB or higher.
  - CPU: Select 1.
- Click Done.
Scroll to Execution environment:
- Select Default (recommended).
In the Revision scaling section:
- Minimum number of instances: Enter 0.
- Maximum number of instances: Enter 100 (or adjust based on expected load).
Click Create.
Wait for the service to be created (1-2 minutes).
After the service is created, the inline code editor opens automatically.

Add function code

Enter main in Function entry point

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

Click Deploy to save and deploy the function.
Wait for deployment to complete (2-3 minutes).

Note: The Pub/Sub trigger configuration automatically creates the necessary subscriptions and permissions.

Create Cloud Scheduler job

Cloud Scheduler publishes messages to the Pub/Sub topic at regular intervals, triggering the Cloud Run function.

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

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)

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

In the Cloud Scheduler console, find your job.
Click Force run to trigger manually.
Wait a few seconds and go to Cloud Run > Services > rippling-activity-collector > Logs.
Verify the function executed successfully.
Check the GCS bucket to confirm logs were written.

Retrieve the Google SecOps service account

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

Get the service account email

Go to SIEM Settings > Feeds.
Click Add New Feed.
Click Configure a single feed.
In the Feed name field, enter a name for the feed (for example, Rippling Activity Logs).
Select Google Cloud Storage V2 as the Source type.
Select Rippling Activity Logs as the Log type.
Click Get Service Account. A unique service account email will be displayed, for example:
```
chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com
```
Copy this email address for use in the next step.

Note: Each Google SecOps instance has a unique service account. Don't use service accounts from other documentation or examples.

Grant IAM permissions to the Google SecOps service account

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

Go to Cloud Storage > Buckets.
Click your bucket name.
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Paste the Google SecOps service account email.
- Assign roles: Select Storage Object Viewer.
Click Save.

Note: If you plan to use the deletion option "Delete transferred files" or "Delete transferred files and empty directories," grant Storage Object Admin role instead of Storage Object Viewer.

Configure a feed in Google SecOps to ingest Rippling Activity Logs

Go to SIEM Settings > Feeds.
Click Add New Feed.
Click Configure a single feed.
In the Feed name field, enter a name for the feed (for example, Rippling Activity Logs).
Select Google Cloud Storage V2 as the Source type.
Select Rippling Activity Logs as the Log type.
Click Next.
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).
    Note: Always include the trailing slash (/) at the end of the URI.
- 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.
    
    Note: If you select a deletion option, the service account must have Storage Object Admin role instead of Storage Object Viewer. Update IAM permissions accordingly.
- 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.
Click Next.
Review your new feed configuration in the Finalize screen, and then click Submit.

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