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    Collect SAP SuccessFactors logs

    Supported in:

    This document explains how to ingest SAP SuccessFactors logs to Google Security Operations using Google Cloud Storage V2.

    SAP SuccessFactors is a cloud-based human capital management (HCM) platform that manages core HR processes, talent management, payroll, and workforce analytics. It generates user activity, authentication, and audit trail logs that can be collected using the OData API.

    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 SAP SuccessFactors with administrator permissions
    • SAP SuccessFactors OData API access enabled for your tenant
    • Your SAP SuccessFactors API server URL (for example, api15.sapsf.com )

    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, sap-successfactors-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 SAP SuccessFactors API credentials

    Determine API server URL

    The SAP SuccessFactors API server URL depends on your data center. Common API server URLs:

    Data Center API Server URL
    DC2 (Amsterdam) https://api2.successfactors.eu
    DC4 (Sydney) https://api4.successfactors.com
    DC8 (Frankfurt) https://api8.successfactors.com
    DC10 (US East) https://api10.successfactors.com
    DC12 (Shanghai) https://api012.successfactors.cn
    DC15 (US West) https://api15.sapsf.com
    DC17 (Singapore) https://api17.sapsf.com
    DC19 (UAE) https://api19.sapsf.com

    Create API user credentials

    1. Sign in to SAP SuccessFactorsas an administrator.
    2. Go to Admin Center > Manage Permission Roles.
    3. Create or select a role that includes the following permissions:
      • Manage Audit Trail: Read access to audit data
      • OData API: Access to the OData API endpoints
    4. Go to Admin Center > Manage Users.
    5. Create a technical user or select an existing user for API integration.
    6. Assign the permission role to the user.

    7. Note the following credentials:

      • Username: The SAP SuccessFactors user ID (format: USERNAME@COMPANY_ID )
      • Password: The user's password
      • Company ID: Your SAP SuccessFactors company identifier

    Verify permissions

    To verify the account has the required permissions:

    1. Sign in to SAP SuccessFactors.
    2. Go to Admin Center > Audit Trail.
    3. If you can see audit trail data and export options, you have the required permissions.
    4. If you cannot see this option, contact your SAP administrator to grant the Manage Audit Trailpermission.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual credentials 
 SF_USER 
 = 
 "USERNAME@COMPANY_ID" 
 SF_PASSWORD 
 = 
 "your-password" 
 API_SERVER 
 = 
 "https://api15.sapsf.com" 
 # Test API access - fetch audit trail metadata 
curl  
-v  
-u  
 " 
 ${ 
 SF_USER 
 } 
 : 
 ${ 
 SF_PASSWORD 
 } 
 " 
  
 \ 
  
 " 
 ${ 
 API_SERVER 
 } 
 /odata/v2/AuditTrail?\$top=1&\$format=json"

    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 sap-sf-logs-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect SAP SuccessFactors 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 on your bucket name (for example, sap-successfactors-logs ).
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email (for example, sap-sf-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 sap-sf-logs-trigger
      • Leave other settings as default
    4. Click Create.

    Create Cloud Run function to collect logs

    The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from SAP SuccessFactors OData 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 sap-sf-logs-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 sap-sf-logs-trigger .
      4. Click Save.

    6. In the Authenticationsection:

      1. Select Require authentication.
      2. Check Identity and Access Management (IAM).

    7. Expand Containers, Networking, Security.

    8. Go to the Securitytab:

      • Service account: Select the service account sap-sf-logs-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
      		sap-successfactors-logs GCS bucket name
      GCS_PREFIX
      		sf-logs Prefix for log files
      STATE_KEY
      		sf-logs/state.json State file path
      SF_API_SERVER
      		https://api15.sapsf.com SAP SuccessFactors API server URL
      SF_USERNAME
      		USERNAME@COMPANY_ID SAP SuccessFactors username
      SF_PASSWORD
      		your-password SAP SuccessFactors password
      MAX_RECORDS
      		5000 Max records per run
      PAGE_SIZE
      		1000 Records per page
      LOOKBACK_HOURS
      		24 Initial lookback period

    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 editorwill open automatically.

    Add function code

    1. Enter mainin the Entry pointfield.

    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 
 import 
  
 base64 
 # 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 
 
 () 
 # Environment variables 
 GCS_BUCKET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 GCS_PREFIX 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'sf-logs' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'sf-logs/state.json' 
 ) 
 SF_API_SERVER 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SF_API_SERVER' 
 ) 
 SF_USERNAME 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SF_USERNAME' 
 ) 
 SF_PASSWORD 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SF_PASSWORD' 
 ) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '5000' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '1000' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '24' 
 )) 
 def 
  
 parse_datetime 
 ( 
 value 
 : 
 str 
 ) 
 - 
> datetime 
 : 
  
 """Parse ISO datetime string to datetime object.""" 
 if 
 value 
 . 
 endswith 
 ( 
 "Z" 
 ): 
 value 
 = 
 value 
 [: 
 - 
 1 
 ] 
 + 
 "+00:00" 
 return 
 datetime 
 . 
 fromisoformat 
 ( 
 value 
 ) 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch SAP SuccessFactors 
 audit logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 SF_API_SERVER 
 , 
 SF_USERNAME 
 , 
 SF_PASSWORD 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 GCS_BUCKET 
 ) 
 # Load state 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 ) 
 # Determine time window 
 now 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 last_time 
 = 
 None 
 if 
 isinstance 
 ( 
 state 
 , 
 dict 
 ) 
 and 
  state 
 
 . 
 get 
 ( 
 "last_event_time" 
 ): 
 try 
 : 
 last_time 
 = 
 parse_datetime 
 ( 
 state 
 [ 
 "last_event_time" 
 ]) 
 # Overlap by 2 minutes to catch any delayed events 
 last_time 
 = 
 last_time 
 - 
 timedelta 
 ( 
 minutes 
 = 
 2 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse last_event_time: 
 { 
 e 
 } 
 " 
 ) 
 if 
 last_time 
 is 
 None 
 : 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 print 
 ( 
 f 
 "Fetching logs from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 # Fetch logs 
 records 
 , 
 newest_event_time 
 = 
 fetch_logs 
 ( 
 api_server 
 = 
 SF_API_SERVER 
 , 
 username 
 = 
 SF_USERNAME 
 , 
 password 
 = 
 SF_PASSWORD 
 , 
 start_time 
 = 
 last_time 
 , 
 end_time 
 = 
 now 
 , 
 page_size 
 = 
 PAGE_SIZE 
 , 
 max_records 
 = 
 MAX_RECORDS 
 , 
 ) 
 if 
 not 
 records 
 : 
 print 
 ( 
 "No new log records found." 
 ) 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 now 
 . 
 isoformat 
 ()) 
 return 
 # Write to GCS as NDJSON 
 timestamp 
 = 
 now 
 . 
 strftime 
 ( 
 '%Y%m 
 %d 
 _%H%M%S' 
 ) 
 object_key 
 = 
 f 
 " 
 { 
 GCS_PREFIX 
 } 
 /logs_ 
 { 
 timestamp 
 } 
 .ndjson" 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 object_key 
 ) 
 ndjson 
 = 
 ' 
 \n 
 ' 
 . 
 join 
 ([ 
 json 
 . 
 dumps 
 ( 
 record 
 , 
 ensure_ascii 
 = 
 False 
 ) 
 for 
 record 
 in 
 records 
 ]) 
 + 
 ' 
 \n 
 ' 
 blob 
 . 
  upload_from_string 
 
 ( 
 ndjson 
 , 
 content_type 
 = 
 'application/x-ndjson' 
 ) 
 print 
 ( 
 f 
 "Wrote 
 { 
 len 
 ( 
 records 
 ) 
 } 
 records to gs:// 
 { 
 GCS_BUCKET 
 } 
 / 
 { 
 object_key 
 } 
 " 
 ) 
 # Update state with newest event time 
 if 
 newest_event_time 
 : 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 newest_event_time 
 ) 
 else 
 : 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 now 
 . 
 isoformat 
 ()) 
 print 
 ( 
 f 
 "Successfully processed 
 { 
 len 
 ( 
 records 
 ) 
 } 
 records" 
 ) 
 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: 
 { 
 e 
 } 
 " 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 last_event_time_iso 
 : 
 str 
 ): 
  
 """Save the last event timestamp to GCS state file.""" 
 try 
 : 
 state 
 = 
 { 
 'last_event_time' 
 : 
 last_event_time_iso 
 } 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 , 
 indent 
 = 
 2 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 print 
 ( 
 f 
 "Saved state: last_event_time= 
 { 
 last_event_time_iso 
 } 
 " 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not save state: 
 { 
 e 
 } 
 " 
 ) 
 def 
  
 fetch_logs 
 ( 
 api_server 
 : 
 str 
 , 
 username 
 : 
 str 
 , 
 password 
 : 
 str 
 , 
 start_time 
 : 
 datetime 
 , 
 end_time 
 : 
 datetime 
 , 
 page_size 
 : 
 int 
 , 
 max_records 
 : 
 int 
 ): 
  
 """ 
 Fetch audit trail logs from SAP SuccessFactors OData API with 
 pagination and rate limiting. 
 Args: 
 api_server: SAP SuccessFactors API server URL 
 username: SAP SuccessFactors username (USERNAME@COMPANY_ID) 
 password: SAP SuccessFactors password 
 start_time: Start time for log query 
 end_time: End time for log query 
 page_size: Number of records per page 
 max_records: Maximum total records to fetch 
 Returns: 
 Tuple of (records list, newest_event_time ISO string) 
 """ 
 base_url 
 = 
 api_server 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 # Build Basic Auth header 
 auth_string 
 = 
 f 
 " 
 { 
 username 
 } 
 : 
 { 
 password 
 } 
 " 
 auth_bytes 
 = 
 auth_string 
 . 
 encode 
 ( 
 'utf-8' 
 ) 
 auth_b64 
 = 
 base64 
 . 
 b64encode 
 ( 
 auth_bytes 
 ) 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Basic 
 { 
 auth_b64 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-SAPSFCollector/1.0' 
 } 
 records 
 = 
 [] 
 newest_time 
 = 
 None 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 skip 
 = 
 0 
 # Format datetime for OData filter 
 start_str 
 = 
 start_time 
 . 
 strftime 
 ( 
 "%Y-%m- 
 %d 
 T%H:%M:%S" 
 ) 
 end_str 
 = 
 end_time 
 . 
 strftime 
 ( 
 "%Y-%m- 
 %d 
 T%H:%M:%S" 
 ) 
 while 
 True 
 : 
 page_num 
 += 
 1 
 if 
 len 
 ( 
 records 
 ) 
> = 
 max_records 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 max_records 
 } 
 )" 
 ) 
 break 
 remaining 
 = 
 min 
 ( 
 page_size 
 , 
 max_records 
 - 
 len 
 ( 
 records 
 )) 
 url 
 = 
 ( 
 f 
 " 
 { 
 base_url 
 } 
 /odata/v2/AuditTrail" 
 f 
 "?$filter=changedDate ge datetime' 
 { 
 start_str 
 } 
 ' and changedDate le datetime' 
 { 
 end_str 
 } 
 '" 
 f 
 "&$top= 
 { 
 remaining 
 } 
 " 
 f 
 "&$skip= 
 { 
 skip 
 } 
 " 
 f 
 "&$format=json" 
 ) 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 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 
 } 
 " 
 ) 
 return 
 [], 
 None 
 data 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 # OData response structure 
 page_results 
 = 
 data 
 . 
 get 
 ( 
 'd' 
 , 
 {}) 
 . 
 get 
 ( 
 'results' 
 , 
 []) 
 if 
 not 
 page_results 
 : 
 print 
 ( 
 f 
 "No more results (empty page)" 
 ) 
 break 
 print 
 ( 
 f 
 "Page 
 { 
 page_num 
 } 
 : Retrieved 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 events" 
 ) 
 records 
 . 
 extend 
 ( 
 page_results 
 ) 
 # Track newest event time 
 for 
 event 
 in 
 page_results 
 : 
 try 
 : 
 changed_date 
 = 
 event 
 . 
 get 
 ( 
 'changedDate' 
 , 
 '' 
 ) 
 # OData datetime format: /Date(1234567890000)/ 
 if 
 changed_date 
 and 
 changed_date 
 . 
 startswith 
 ( 
 '/Date(' 
 ): 
 ms 
 = 
 int 
 ( 
 changed_date 
 . 
 split 
 ( 
 '(' 
 )[ 
 1 
 ] 
 . 
 split 
 ( 
 ')' 
 )[ 
 0 
 ] 
 . 
 split 
 ( 
 '+' 
 )[ 
 0 
 ] 
 . 
 split 
 ( 
 '-' 
 )[ 
 0 
 ]) 
 event_dt 
 = 
 datetime 
 . 
 fromtimestamp 
 ( 
 ms 
 / 
 1000 
 , 
 tz 
 = 
 timezone 
 . 
 utc 
 ) 
 event_time 
 = 
 event_dt 
 . 
 isoformat 
 () 
 if 
 newest_time 
 is 
 None 
 or 
 parse_datetime 
 ( 
 event_time 
 ) 
> parse_datetime 
 ( 
 newest_time 
 ): 
 newest_time 
 = 
 event_time 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse event time: 
 { 
 e 
 } 
 " 
 ) 
 # Check for more results 
 if 
 len 
 ( 
 page_results 
 ) 
< remaining 
 : 
 print 
 ( 
 f 
 "Reached last page (size= 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 < limit= 
 { 
 remaining 
 } 
 )" 
 ) 
 break 
 skip 
 += 
 len 
 ( 
 page_results 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching logs: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total records from 
 { 
 page_num 
 } 
 pages" 
 ) 
 return 
 records 
 , 
 newest_time

      • 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 will publish 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 sap-sf-logs-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 sap-sf-logs-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 on sap-sf-logs-collector .
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for:

       Fetching logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Page 1: Retrieved X events
Wrote X records to gs://sap-successfactors-logs/sf-logs/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click on your bucket name ( sap-successfactors-logs ).

    10. Navigate to the sf-logs/ folder.

    11. Verify that a new .ndjson file was 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 in SAP SuccessFactors - HTTP 429: Rate limiting - function will automatically retry with backoff - Missing environment variables: Check all required variables are set

    Configure a feed in Google SecOps to ingest SAP SuccessFactors 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, SAP SuccessFactors Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select SAP SuccessFactorsas 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.

    9. Click Next.

    10. Specify values for the following input parameters:

      • Storage bucket URL: Enter the GCS bucket URI with the prefix path:

         gs://sap-successfactors-logs/sf-logs/
        • Replace:
          • sap-successfactors-logs : Your GCS bucket name.
          • sf-logs : Optional prefix/folder path where logs are stored (leave empty for root).

      • 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

    11. Click Next.

    12. Review your new feed configuration in the Finalizescreen, and then click Submit.

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

    1. Go to Cloud Storage > Buckets.
    2. Click on 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.

    UDM mapping table

    Log Field UDM Mapping Logic
    module, functional_area, functional_sub_area, context_1_value, context_2_value, context_3_value, context_4_value, context_5_value, new_value, old_value, operation_performed, effective_start_date, effective_sequence
    		 additional.fields Tokens created from each field and merged if not empty and conditions met
    changed_by_user_first_name
    		 intermediary_1.user.first_name Value copied directly if not empty and secondary user present
    changed_by_user_last_name
    		 intermediary_1.user.last_name Value copied directly if not empty and secondary user present
    changed_by_user_username
    		 intermediary_1.user.userid Value copied directly if not empty and secondary user present
    proxy_user_first_name
    		 intermediary_2.user.first_name Value copied directly if not empty
    proxy_user_last_name
    		 intermediary_2.user.last_name Value copied directly if not empty
    proxy_user_username
    		 intermediary_2.user.userid Value copied directly if not empty
    		metadata.event_type Set to "GENERIC_EVENT", overridden to "USER_RESOURCE_ACCESS" if context_1_key == "Role" or field_name == "Role", or "RESOURCE_PERMISSIONS_CHANGE" if subject user fields present and changed_by_user_username not present
    new_value
    		 permission.name Value copied from new_value if field_name == "Permission"
    secondary_user_email
    		 principal.user.email_addresses Value copied directly if not empty
    secondary_user_provisioner_id
    		 principal.user.userid Value copied directly if not empty
    context_1_value, new_value
    		 role.name Value from context_1_value if context_1_key == "Role"; otherwise from new_value if field_name == "Role name" or "Role"
    old_value, new_value
    		 target.group.attribute.labels Merged with tokens from old_value or new_value based on field_name
    context_1_value, new_value
    		 target.group.group_display_name Value from context_1_value if context_1_key == "Group"; otherwise from new_value if field_name == "Group" or "Group name"
    context_3_value
    		 target.resource.name Value copied from context_3_value if context_3_key == "Feature Name"
    context_2_value
    		 target.resource.product_object_id Value copied from context_2_value if context_2_key == "Feature Id"
    old_value, new_value
    		 target.user.attribute.labels Merged with tokens from old_value or new_value based on field_name
    new_value
    		 target.user.attribute.permissions Merged with permission object created from new_value if field_name == "Permission"
    context_1_value, new_value
    		 target.user.attribute.roles Merged with role object created from context_1_value if context_1_key == "Role", or from new_value if field_name == "Role name" or "Role"
    subject_user_first_name, first_name, first_name
    		 target.user.first_name Value from subject_user_first_name if not empty; otherwise extracted from context_1_value using grok if context_1_key == "Proxy Rights For"; otherwise extracted from context_2_value using grok if context_2_key == "User name"
    subject_user_last_name, last_name, last_name
    		 target.user.last_name Value from subject_user_last_name if not empty; otherwise extracted from context_1_value using grok if context_1_key == "Proxy Rights For"; otherwise extracted from context_2_value using grok if context_2_key == "User name"
    subject_user_id, context_1_value
    		 target.user.userid Value from subject_user_id if not empty; otherwise from context_1_value if context_1_key == "User"
    		metadata.product_name Set to "SuccessFactors"
    		metadata.vendor_name Set to "SAP"

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

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