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    Collect Proofpoint Secure Email Relay logs

    Supported in:

    This document explains how to ingest Proofpoint Secure Email Relay logs to Google Security Operations using Google Cloud Storage V2.

    Proofpoint Secure Email Relay (SER) is an outbound email security service that provides encryption, data loss prevention (DLP), and compliance enforcement for messages sent from your organization. The parser extracts fields from SER message tracking data and maps them to the Unified Data Model (UDM), capturing email metadata, throughput metrics, delivery status, and user activity.

    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 services, Pub/Sub topics, and Cloud Scheduler jobs
    • Privileged access to Proofpoint Secure Email Relay with API key access

    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, proofpoint-ser-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 Proofpoint SER API credentials

    Obtain API credentials

    1. Sign in to the Proofpoint Secure Email Relay admin portal with administrator credentials.
    2. Navigate to Settings > API Keys.
    3. Click Generateor Create API Key.

    4. Copy and securely store the following credentials:

      • API Key: Copy this value
      • API Secret: Copy this value

    Verify permissions

    To verify the API credentials have the required permissions:

    1. Sign in to the Proofpoint SER admin portal.
    2. Navigate to Settings > API Keys.
    3. Confirm the API key is listed and has an Activestatus.
    4. Verify the key has access to the message tracking and reporting endpoints.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual credentials 
 API_KEY 
 = 
 "<your-api-key>" 
 API_SECRET 
 = 
 "<your-api-secret>" 
 # Test API access - retrieve recent messages 
curl  
-v  
-u  
 " 
 ${ 
 API_KEY 
 } 
 : 
 ${ 
 API_SECRET 
 } 
 " 
  
 \ 
  
 "https://ser-api.proofpoint.com/v1/messages?startDate= 
 $( 
date  
-u  
-v-1H  
+%Y-%m-%dT%H:%M:%SZ ) 
& endDate= 
 $( 
date  
-u  
+%Y-%m-%dT%H:%M:%SZ ) 
 "

    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 proofpoint-ser-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect Proofpoint Secure Email Relay 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 writepermissions on the GCS bucket:

    1. Go to Cloud Storage > Buckets.
    2. Click on your bucket name (for example, proofpoint-ser-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, proofpoint-ser-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 proofpoint-ser-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 message logs from the Proofpoint SER 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 proofpoint-ser-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 Pub/Sub topic ( proofpoint-ser-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 ( proofpoint-ser-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
      		proofpoint-ser-logs GCS bucket name
      GCS_PREFIX
      		ser-logs Prefix for log files
      STATE_KEY
      		ser-logs/state.json State file path
      API_KEY
      		your-api-key Proofpoint SER API key
      API_SECRET
      		your-api-secret Proofpoint SER API secret
      MAX_RECORDS
      		1000 Max records per run
      PAGE_SIZE
      		100 Records per page
      LOOKBACK_HOURS
      		1 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' 
 , 
 'ser-logs' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'ser-logs/state.json' 
 ) 
 API_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'API_KEY' 
 ) 
 API_SECRET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'API_SECRET' 
 ) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '1000' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '100' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '1' 
 )) 
 API_BASE 
 = 
 "https://ser-api.proofpoint.com/v1" 
 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 Proofpoint SER 
 message logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 API_KEY 
 , 
 API_SECRET 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 # Get GCS bucket 
 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" 
 ]) 
 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 
 () 
 } 
 " 
 ) 
 # Build auth header (Basic auth with API key and secret) 
 auth_string 
 = 
 f 
 " 
 { 
 API_KEY 
 } 
 : 
 { 
 API_SECRET 
 } 
 " 
 auth_bytes 
 = 
 auth_string 
 . 
 encode 
 ( 
 'utf-8' 
 ) 
 auth_b64 
 = 
 base64 
 . 
 b64encode 
 ( 
 auth_bytes 
 ) 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 # Fetch messages 
 records 
 , 
 newest_event_time 
 = 
 fetch_messages 
 ( 
 auth_b64 
 = 
 auth_b64 
 , 
 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_messages 
 ( 
 auth_b64 
 : 
 str 
 , 
 start_time 
 : 
 datetime 
 , 
 end_time 
 : 
 datetime 
 , 
 page_size 
 : 
 int 
 , 
 max_records 
 : 
 int 
 ): 
  
 """ 
 Fetch message logs from Proofpoint SER API with pagination. 
 Args: 
 auth_b64: Base64-encoded API key:secret for Basic auth 
 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) 
 """ 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Basic 
 { 
 auth_b64 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'Content-Type' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-ProofpointSERCollector/1.0' 
 , 
 } 
 records 
 = 
 [] 
 newest_time 
 = 
 None 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 offset 
 = 
 0 
 start_date 
 = 
 start_time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 T%H:%M:%SZ' 
 ) 
 end_date 
 = 
 end_time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 T%H:%M:%SZ' 
 ) 
 while 
 True 
 : 
 page_num 
 += 
 1 
 if 
 len 
 ( 
 records 
 ) 
> = 
 max_records 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 max_records 
 } 
 )" 
 ) 
 break 
 current_limit 
 = 
 min 
 ( 
 page_size 
 , 
 max_records 
 - 
 len 
 ( 
 records 
 )) 
 url 
 = 
 f 
 " 
 { 
 API_BASE 
 } 
 /messages?startDate= 
 { 
 start_date 
 } 
& endDate= 
 { 
 end_date 
 } 
& offset= 
 { 
 offset 
 } 
& limit= 
 { 
 current_limit 
 } 
 " 
 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' 
 )) 
 if 
 isinstance 
 ( 
 data 
 , 
 list 
 ): 
 page_results 
 = 
 data 
 else 
 : 
 page_results 
 = 
 data 
 . 
 get 
 ( 
 'messages' 
 , 
 data 
 . 
 get 
 ( 
 'results' 
 , 
 data 
 . 
 get 
 ( 
 'data' 
 , 
 []))) 
 if 
 not 
 page_results 
 : 
 print 
 ( 
 f 
 "No more results (empty page)" 
 ) 
 break 
 print 
 ( 
 f 
 "Page 
 { 
 page_num 
 } 
 : Retrieved 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 messages" 
 ) 
 records 
 . 
 extend 
 ( 
 page_results 
 ) 
 # Track newest event time 
 for 
 event 
 in 
 page_results 
 : 
 try 
 : 
 event_time 
 = 
 event 
 . 
 get 
 ( 
 'date' 
 ) 
 or 
 event 
 . 
 get 
 ( 
 'timestamp' 
 ) 
 or 
 event 
 . 
 get 
 ( 
 'sentDate' 
 ) 
 if 
 event_time 
 : 
 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 
 ) 
< current_limit 
 : 
 print 
 ( 
 f 
 "Reached last page (size= 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 < limit= 
 { 
 current_limit 
 } 
 )" 
 ) 
 break 
 offset 
 += 
 len 
 ( 
 page_results 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching messages: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total messages 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 proofpoint-ser-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 Pub/Sub topic ( proofpoint-ser-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 your function name ( proofpoint-ser-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 messages
Wrote X records to gs://proofpoint-ser-logs/ser-logs/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click on your bucket name ( proofpoint-ser-logs ).

    10. Navigate to the prefix folder ( ser-logs/ ).

    11. Verify that a new .ndjson file was created with the current timestamp.

    If you see errors in the logs:

    • HTTP 401: Check the API key and API secret in environment variables
    • HTTP 403: Verify the API key has access to the message tracking endpoint
    • HTTP 429: Rate limiting - function will automatically retry with backoff
    • Missing environment variables: Check all required variables are set

    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, Proofpoint SER Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select ProofPoint Secure Email Relayas the Log type.
    7. Click Get Service Account.

    8. A unique service account email will be displayed, for example:

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

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

    10. Click Next.

    11. Specify values for the following input parameters:

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

         gs://proofpoint-ser-logs/ser-logs/
        • Replace:
          • proofpoint-ser-logs : Your GCS bucket name.
          • ser-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

    12. Click Next.

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

    3The Google SecOps service account needs Storage Object Viewerrole on your GCS bucket.

    1. Go to Cloud Storage > Buckets.
    2. Click on your bucket name (for example, proofpoint-ser-logs ).
    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
    status, details, data.throughputLimit, data.throughput, data.totalThroughput, log_metadata.totalThroughput, data.averageDailyThroughput, data.throughputForecast, data.remainingThroughput, data.acceptedThroughput, data.licenseStartDate, data.licenseEndDate, data.average7DayThroughput, data.average30DayThroughput, data.requestedMessages, data.acceptedMessages, data.sentMessages, data.deliveredMessages, data.avgAcceptedMessageSize, data.blockedMessages, data.quarantinedMessages, data.rejectedMessages, data.requestedThroughput, data.totalMessages, data.undeliveredMessages
    		 additional.fields Merged labels from status map as string values, details nested map as flattened keys with string values, and various data fields as string or number values
    desc, data.name
    		 metadata.description Value from desc if not empty, else data.name
    event_type
    		 metadata.event_type Set to EMAIL_TRANSACTION if user_present is true, else GENERIC_EVENT
    		metadata.product_name Set to "PROOFPOINT SER"
    		metadata.vendor_name Set to "PROOFPOINT"
    fromEnvelope
    		 network.email.bounce_address Value from fromEnvelope if matches email pattern
    fromHeader
    		 network.email.from Value from fromHeader if matches email pattern
    applicationName
    		 principal.administrative_domain Value copied directly
    principal_host
    		 principal.asset.hostname Value copied directly
    principal_host
    		 principal.hostname Value copied directly
    principal_port
    		 principal.port Value from principal_port converted to integer
    userId, data.relayUserId
    		 principal.user.product_object_id Value from userId if not empty, else data.relayUserId
    applicationUserName
    		 principal.user.user_display_name Value copied directly
    senderName
    		 target.administrative_domain Value copied directly
    senderId
    		 target.user.product_object_id Value copied directly

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