Collect MuleSoft Anypoint platform logs

Supported in:

Google secops SIEM

Note: This feature is covered by Pre-GA Offerings Terms of the Google Security Operations Service Specific Terms. Pre-GA features might have limited support, and changes to pre-GA features might not be compatible with other pre-GA versions. For more information, see the Google SecOps Technical Support Service guidelines and the Google SecOps Service Specific Terms.

Note: Because MuleSoft is a log type without a prebuilt parser, this guide will only cover log ingestion. You must configure a custom parser to convert MuleSoft logs to UDM.

This document explains how to ingest audit-trail events from MuleSoft Anypoint platform logs to Google Security Operations using Google Cloud Storage.

Before you begin

Make sure 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
• Permissions to create service accounts
• Privileged access to MuleSoft Anypoint Platform

Get the MuleSoft Organization ID

1. Sign in to the Anypoint Platform.
2. Go to Access Management > Organizations.
3. In the Business Groups table, click your organization's name.
4. Copy the Organization ID (for example, 0a12b3c4-d5e6-789f-1021-1a2b34cd5e6f).

Alternatively, go to MuleSoft Business Groups and copy the ID from the URL.

Create the MuleSoft Connected App

1. Sign in to the Anypoint Platform.
2. Go to Access Management > Connected Apps > Create App.
3. Provide the following configuration details:
   • App name: Enter a unique name (for example, Google SecOps export).
   • Select App acts on its own behalf (client credentials).
4. Click Add scopes > Audit Log Viewer > Next.
5. Select every Business Group whose logs you need.
6. Click Next > Add scopes.

7. Click Save and copy the Client ID and Client Secret.

   Note: Additional scopes may be required based on organization permissions. If you encounter permission errors, verify that the Connected App has been granted access to all required Business Groups.

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, mulesoft-audit-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.

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

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 mulesoft-logs-collector-sa.
   • Service account description: Enter Service account for Cloud Run function to collect MuleSoft Anypoint logs.
4. Click Create and Continue.
5. 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.
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 Permissions tab.
4. Click Grant access.
5. Provide the following configuration details:
   • Add principals: Enter the service account email (for example, mulesoft-logs-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 mulesoft-audit-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 MuleSoft Anypoint 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 Configure section, provide the following configuration details:

   Setting	Value
   Service name	mulesoft-audit-collector
   Region	Select region matching your GCS bucket (for example, us-central1)
   Runtime	Select Python 3.12 or 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 mulesoft-audit-trigger.
   4. Click Save.

6. In the Authentication section:

   1. Select Require authentication.
   2. Check Identity and Access Management (IAM).
   Note: Pub/Sub automatically handles authentication when invoking the function.

7. Scroll down and expand Containers, Networking, Security.

8. Go to the Security tab:

   • Service account: Select the service account mulesoft-logs-collector-sa.

9. Go to the Containers tab:

   1. Click Variables & Secrets.
   2. Click + Add variable for each environment variable:

   Variable Name	Example Value
   MULE_ORG_ID	your_org_id
   CLIENT_ID	your_client_id
   CLIENT_SECRET	your_client_secret
   GCS_BUCKET	mulesoft-audit-logs

10. Scroll down in the Variables & Secrets tab to Requests:

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

11. Go to the Settings tab in Containers:

    • In the Resources section:
      • Memory: Select 512 MiB or higher.
      • CPU: Select 1.
    • Click Done.

12. Scroll to Execution environment:

    • Select Default (recommended).

13. In the Revision scaling section:

    • 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 editor opens automatically.

Add function code

1. Enter main in 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, timedelta, timezone
   import uuid
   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()
   
   # MuleSoft API endpoints
   TOKEN_URL = "https://anypoint.mulesoft.com/accounts/api/v2/oauth2/token"
   
   @functions_framework.cloud_event
   def main(cloud_event):
       """
       Cloud Run function triggered by Pub/Sub to fetch MuleSoft audit logs and write to GCS.
   
       Args:
           cloud_event: CloudEvent object containing Pub/Sub message
       """
   
       # Get environment variables
       org_id = os.environ.get('MULE_ORG_ID')
       client_id = os.environ.get('CLIENT_ID')
       client_secret = os.environ.get('CLIENT_SECRET')
       bucket_name = os.environ.get('GCS_BUCKET')
   
       if not all([org_id, client_id, client_secret, bucket_name]):
           print('Error: Missing required environment variables')
           return
   
       query_url = f"https://anypoint.mulesoft.com/audit/v2/organizations/{org_id}/query"
   
       try:
           # Get GCS bucket
           bucket = storage_client.bucket(bucket_name)
   
           # Get OAuth token
           token = get_token(client_id, client_secret)
   
           # Calculate time range (last 24 hours)
           now = datetime.now(timezone.utc).replace(microsecond=0)
           start = now - timedelta(days=1)
   
           print(f'Fetching audit logs from {start.isoformat()} to {now.isoformat()}')
   
           # Fetch audit logs
           events = list(fetch_audit(query_url, token, start, now))
   
           # Upload to GCS
           if events:
               upload_to_gcs(bucket, events, start)
               print(f'Uploaded {len(events)} events')
           else:
               print('No events in the last 24 hours')
   
       except Exception as e:
           print(f'Error processing logs: {str(e)}')
           raise
   
   def get_token(client_id, client_secret):
       """Get OAuth 2.0 access token from MuleSoft."""
       data = {
           'grant_type': 'client_credentials',
           'client_id': client_id,
           'client_secret': client_secret
       }
   
       encoded_data = urllib3.request.urlencode(data).encode('utf-8')
   
       backoff = 1.0
       max_retries = 3
   
       for attempt in range(max_retries):
           try:
               response = http.request(
                   'POST',
                   TOKEN_URL,
                   body=encoded_data,
                   headers={'Content-Type': 'application/x-www-form-urlencoded'}
               )
   
               if response.status == 429:
                   retry_after = int(response.headers.get('Retry-After', str(int(backoff))))
                   print(f'Rate limited (429) on token request. Retrying after {retry_after}s...')
                   time.sleep(retry_after)
                   backoff = min(backoff * 2, 30.0)
                   continue
   
               if response.status != 200:
                   raise Exception(f'Failed to get token: {response.status} - {response.data.decode()}')
   
               token_data = json.loads(response.data.decode('utf-8'))
               return token_data['access_token']
   
           except Exception as e:
               if attempt == max_retries - 1:
                   raise
               print(f'Token request failed (attempt {attempt + 1}/{max_retries}): {e}')
               time.sleep(backoff)
               backoff = min(backoff * 2, 30.0)
   
       raise Exception('Failed to get token after maximum retries')
   
   def fetch_audit(query_url, token, start, end):
       """Fetch audit logs from MuleSoft API with pagination."""
       headers = {
           'Authorization': f'Bearer {token}',
           'Content-Type': 'application/json'
       }
   
       body = {
           'startDate': f"{start.isoformat(timespec='milliseconds')}Z",
           'endDate': f"{end.isoformat(timespec='milliseconds')}Z",
           'limit': 200,
           'offset': 0,
           'ascending': False
       }
   
       backoff = 1.0
   
       while True:
           try:
               response = http.request(
                   'POST',
                   query_url,
                   body=json.dumps(body).encode('utf-8'),
                   headers=headers
               )
   
               # Handle rate limiting with exponential backoff
               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)
                   continue
   
               backoff = 1.0
   
               if response.status != 200:
                   print(f'HTTP Error: {response.status}')
                   response_text = response.data.decode('utf-8')
                   print(f'Response body: {response_text}')
                   break
   
               data = json.loads(response.data.decode('utf-8'))
   
               if not data.get('data'):
                   break
   
               yield from data['data']
               body['offset'] += body['limit']
   
           except Exception as e:
               print(f'Error fetching audit logs: {e}')
               break
   
   def upload_to_gcs(bucket, events, timestamp):
       """Upload events to GCS as compressed JSON."""
       import gzip
       import io
   
       # Create blob name with timestamp and UUID
       blob_name = f"{timestamp.strftime('%Y/%m/%d')}/mulesoft-audit-{uuid.uuid4()}.json.gz"
   
       # Compress events
       buf = io.BytesIO()
       with gzip.GzipFile(fileobj=buf, mode='w') as gz:
           for event in events:
               gz.write((json.dumps(event) + '\n').encode('utf-8'))
   
       buf.seek(0)
   
       # Upload to GCS
       blob = bucket.blob(blob_name)
       blob.upload_from_file(buf, content_type='application/gzip')
   
       print(f'Uploaded to gs://{bucket.name}/{blob_name}')
   

   • Second file: requirements.txt:

   functions-framework==3.*
   google-cloud-storage==2.*
   urllib3>=2.0.0
   

3. Click Deploy to save and deploy the function.

4. Wait for deployment to complete (2-3 minutes).

Important considerations

Rate limiting: The Audit Log Query endpoint applies rate limits per IP in the three control planes. The US control plane allows 700 requests per minute per IP, while EU and Gov control planes allow 40 requests per minute per IP. The function implements exponential backoff to handle rate limiting automatically.

Token expiration: Access tokens usually expire about 30 to 60 minutes after being issued. The function requests a new token for each execution. For production deployments with frequent executions, consider implementing token caching with refresh logic.

Audit log retention: Audit logs have a default retention period of one year. If your organization was created before July 10, 2023 and you didn't manually change the retention period, the retention period is six years. Download logs periodically if you need to retain them beyond the configured retention period.

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	daily-mulesoft-audit-export
   Region	Select same region as Cloud Run function
   Frequency	0 2 * * * (runs daily at 02:00 UTC)
   Timezone	Select timezone (UTC recommended)
   Target type	Pub/Sub
   Topic	Select the topic mulesoft-audit-trigger
   Message body	{} (empty JSON object)

4. Click Create.

Test the scheduler job

1. In the Cloud Scheduler console, find your job.
2. Click Force run to trigger manually.
3. Wait a few seconds and go to Cloud Run > Services > mulesoft-audit-collector > Logs.
4. Verify the function executed successfully.
5. 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

1. Go to SIEM Settings > Feeds.
2. Click Add New Feed.
3. Click Configure a single feed.
4. In the Feed name field, enter a name for the feed (for example, MuleSoft Logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select Mulesoft as the Log type.

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

   [e-mail]
   

8. 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.

1. Go to Cloud Storage > Buckets.
2. Click your bucket name.
3. Go to the Permissions tab.
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.

   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 the MuleSoft logs

1. Go to SIEM Settings > Feeds.
2. Click Add New Feed.
3. Click Configure a single feed.
4. In the Feed name field, enter a name for the feed (for example, MuleSoft Logs).
5. Select Google Cloud Storage V2 as the Source type.
6. Select Mulesoft as the Log type.
7. Click Next.

8. Specify values for the following input parameters:

   • Storage bucket URL: Enter the GCS bucket URI:

     gs://mulesoft-audit-logs/
     

     • Replace mulesoft-audit-logs with the actual name of the bucket.
     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.

   • Ingestion labels: The label to be applied to the events from this feed.

9. Click Next.

10. 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.