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    Collect MuleSoft Anypoint platform logs

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    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 Groupstable, 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 Saveand copy the Client IDand Client Secret.

    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.

    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 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 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, 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 Configuresection, 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 3.12or 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 mulesoft-audit-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 mulesoft-logs-collector-sa .

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor 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 & 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 
 , 
 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 Deployto 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 Schedulerconsole, find your job.
    2. Click Force runto 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.

    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, MuleSoft Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Mulesoftas 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 the MuleSoft 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, MuleSoft Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Mulesoftas 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.

      • 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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