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    Collect Fastly Next-Gen WAF (formerly Signal Sciences) logs

    Supported in:

    This document explains how to ingest Fastly Next-Gen WAF (formerly known as Signal Sciences) logs to Google Security Operations using Google Cloud Storage V2.

    Fastly Next-Gen WAF is a cloud-based web application firewall that provides real-time threat detection and blocking for web applications, APIs, and microservices. It uses a signal-based approach to identify and mitigate attacks such as SQL injection, cross-site scripting, account takeover, and application abuse. The Signal Sciences REST API provides programmatic access to request feed data, which contains detailed information about flagged and blocked requests.

    Before you begin

    Make sure 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 the Fastly Next-Gen WAF dashboard with API access permissions
    • A Fastly Next-Gen WAF account with a corp name and at least one site configured

    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, sigsci-waf-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 Fastly Next-Gen WAF API credentials

    Get API access token

    1. Sign in to the Fastly Next-Gen WAF dashboard .
    2. Click your username in the upper-right corner, then select My Profile.
    3. Go to API Access Tokens.
    4. Click Add API access token.
    5. Enter a descriptive name for the token (for example, Google SecOps Integration ).
    6. Click Create API access token.

    7. Copy and save the following details in a secure location:

      • API token: The generated token value (shown only once)
      • Email address: Your account email address used for authentication

    Get corp and site names

    1. Sign in to the Fastly Next-Gen WAF dashboard .
    2. Click Manage > Corp > Corp Overview.
    3. Note the Corp short namedisplayed on the page (for example, my_corp ).
    4. Go to Manage > Sites.
    5. Note the Site short namefor the site you want to collect logs from (for example, my_site ).

    Verify permissions

    To verify the account has the required permissions:

    1. Sign in to the Fastly Next-Gen WAF dashboard .
    2. Go to Manage > Corp > Corp Users.
    3. Find your user account in the list.
    4. Verify that your role is Admin, Owner, or Observer. These roles have the required API access to retrieve request feed data.
    5. If your role does not have API access, contact your Fastly Next-Gen WAF administrator to grant the appropriate role.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual credentials 
 SIGSCI_EMAIL 
 = 
 "your-email@example.com" 
 SIGSCI_TOKEN 
 = 
 "your-api-token" 
 SIGSCI_CORP 
 = 
 "your-corp-name" 
 SIGSCI_SITE 
 = 
 "your-site-name" 
 # Test API access - get site overview 
curl  
-v  
 \ 
  
-H  
 "x-api-user: 
 ${ 
 SIGSCI_EMAIL 
 } 
 " 
  
 \ 
  
-H  
 "x-api-token: 
 ${ 
 SIGSCI_TOKEN 
 } 
 " 
  
 \ 
  
 "[https://dashboard.signalsciences.net/api/v0/corps/ 
$ ](https://dashboard.signalsciences.net/api/v0/corps/ 
$ ){SIGSCI_CORP}/sites/ 
 ${ 
 SIGSCI_SITE 
 } 
 "

    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 sigsci-waf-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect Fastly Next-Gen WAF 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, sigsci-waf-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, sigsci-waf-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 sigsci-waf-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 request feed data from the Fastly Next-Gen WAF API and write the logs 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 sigsci-waf-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 sigsci-waf-logs-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 sigsci-waf-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
      		sigsci-waf-logs GCS bucket name
      GCS_PREFIX
      		sigsci-waf Prefix for log files
      STATE_KEY
      		sigsci-waf/state.json State file path
      SIGSCI_EMAIL
      		your-email@example.com Signal Sciences API email
      SIGSCI_TOKEN
      		your-api-token Signal Sciences API token
      SIGSCI_CORP
      		your-corp-name Signal Sciences corp short name
      SIGSCI_SITE
      		your-site-name Signal Sciences site short name
      MAX_RECORDS
      		10000 Max records per run
      PAGE_SIZE
      		1000 Records per page (max 1000)
      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:

      • 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 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' 
 , 
 'sigsci-waf' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'sigsci-waf/state.json' 
 ) 
 SIGSCI_EMAIL 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SIGSCI_EMAIL' 
 ) 
 SIGSCI_TOKEN 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SIGSCI_TOKEN' 
 ) 
 SIGSCI_CORP 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SIGSCI_CORP' 
 ) 
 SIGSCI_SITE 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'SIGSCI_SITE' 
 ) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '10000' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '1000' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '24' 
 )) 
 # Signal Sciences API base URL 
 API_BASE 
 = 
 '[https://dashboard.signalsciences.net/api/v0](https://dashboard.signalsciences.net/api/v0)' 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch Fastly Next-Gen WAF 
 request feed data and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 SIGSCI_EMAIL 
 , 
 SIGSCI_TOKEN 
 , 
 SIGSCI_CORP 
 , 
 SIGSCI_SITE 
 ]): 
 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 
 = 
 datetime 
 . 
 fromisoformat 
 ( 
 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 
 ) 
 # Convert to Unix epoch seconds (Signal Sciences API uses seconds) 
 from_epoch 
 = 
 int 
 ( 
 last_time 
 . 
 timestamp 
 ()) 
 until_epoch 
 = 
 int 
 ( 
 now 
 . 
 timestamp 
 ()) 
 print 
 ( 
 f 
 "Fetching request feed from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Corp: 
 { 
 SIGSCI_CORP 
 } 
 , Site: 
 { 
 SIGSCI_SITE 
 } 
 " 
 ) 
 # Fetch request feed 
 records 
 , 
 newest_event_time 
 = 
 fetch_request_feed 
 ( 
 corp 
 = 
 SIGSCI_CORP 
 , 
 site 
 = 
 SIGSCI_SITE 
 , 
 from_epoch 
 = 
 from_epoch 
 , 
 until_epoch 
 = 
 until_epoch 
 , 
 page_size 
 = 
 PAGE_SIZE 
 , 
 max_records 
 = 
 MAX_RECORDS 
 , 
 ) 
 if 
 not 
 records 
 : 
 print 
 ( 
 "No new request feed 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_request_feed 
 ( 
 corp 
 : 
 str 
 , 
 site 
 : 
 str 
 , 
 from_epoch 
 : 
 int 
 , 
 until_epoch 
 : 
 int 
 , 
 page_size 
 : 
 int 
 , 
 max_records 
 : 
 int 
 ): 
  
 """ 
 Fetch request feed from Fastly Next-Gen WAF (Signal Sciences) API 
 with cursor-based pagination and rate limiting. 
 Args: 
 corp: Signal Sciences corp short name 
 site: Signal Sciences site short name 
 from_epoch: Start time as Unix epoch seconds 
 until_epoch: End time as Unix epoch seconds 
 page_size: Number of records per page (max 1000) 
 max_records: Maximum total records to fetch (max 10000) 
 Returns: 
 Tuple of (records list, newest_event_time ISO string) 
 """ 
 headers 
 = 
 { 
 'x-api-user' 
 : 
 SIGSCI_EMAIL 
 , 
 'x-api-token' 
 : 
 SIGSCI_TOKEN 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-SignalSciencesWAFCollector/1.0' 
 } 
 records 
 = 
 [] 
 newest_time 
 = 
 None 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 # Initial URL with time range parameters 
 url 
 = 
 f 
 " 
 { 
 API_BASE 
 } 
 /corps/ 
 { 
 corp 
 } 
 /sites/ 
 { 
 site 
 } 
 /feed/requests?from= 
 { 
 from_epoch 
 } 
& until= 
 { 
 until_epoch 
 } 
& limit= 
 { 
 min 
 ( 
 page_size 
 , 
  
 1000 
 ) 
 } 
 " 
 while 
 url 
 : 
 page_num 
 += 
 1 
 if 
 len 
 ( 
 records 
 ) 
> = 
 max_records 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 max_records 
 } 
 )" 
 ) 
 break 
 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' 
 )) 
 page_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 
 ) 
 } 
 events" 
 ) 
 records 
 . 
 extend 
 ( 
 page_results 
 ) 
 # Track newest event time 
 for 
 event 
 in 
 page_results 
 : 
 try 
 : 
 event_timestamp 
 = 
 event 
 . 
 get 
 ( 
 'timestamp' 
 ) 
 if 
 event_timestamp 
 : 
 event_dt 
 = 
 datetime 
 . 
 fromtimestamp 
 ( 
 event_timestamp 
 , 
 tz 
 = 
 timezone 
 . 
 utc 
 ) 
 event_time 
 = 
 event_dt 
 . 
 isoformat 
 () 
 if 
 newest_time 
 is 
 None 
 or 
 event_dt 
> datetime 
 . 
 fromisoformat 
 ( 
 newest_time 
 ): 
 newest_time 
 = 
 event_time 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse event time: 
 { 
 e 
 } 
 " 
 ) 
 # Cursor-based pagination using next URI 
 next_url 
 = 
 data 
 . 
 get 
 ( 
 'next' 
 , 
 {}) 
 . 
 get 
 ( 
 'uri' 
 , 
 '' 
 ) 
 if 
 next_url 
 : 
 url 
 = 
 f 
 "[https://dashboard.signalsciences.net](https://dashboard.signalsciences.net) 
 { 
 next_url 
 } 
 " 
 else 
 : 
 print 
 ( 
 "No more pages (no next cursor)" 
 ) 
 break 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching request feed: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total records from 
 { 
 page_num 
 } 
 pages" 
 ) 
 return 
 records 
 , 
 newest_time
      • 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 sigsci-waf-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 sigsci-waf-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 sigsci-waf-collector .
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for:

       Fetching request feed from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Corp: my_corp, Site: my_site
Page 1: Retrieved X events
Wrote X records to gs://sigsci-waf-logs/sigsci-waf/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click on your bucket name ( sigsci-waf-logs ).

    10. Navigate to the sigsci-waf/ 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 email and token in environment variables
    • HTTP 403: Verify the account has Admin, Owner, or Observer role in the Fastly Next-Gen WAF dashboard
    • 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 Fastly Next-Gen WAF 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, Signal Sciences WAF Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Signal Sciences WAFas 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://sigsci-waf-logs/sigsci-waf/
        • Replace:
          • sigsci-waf-logs : Your GCS bucket name.
          • sigsci-waf : 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
    		metadata.event_type Set to "NETWORK_HTTP"
    		metadata.vendor_name Set to "Signal Sciences"
    		metadata.product_name Set to "WAF"
    id
    		 metadata.product_log_id Value copied directly
    timestamp
    		 metadata.event_timestamp Converted from Unix epoch seconds
    remoteIP
    		 principal.ip Value copied directly
    remoteHostname
    		 principal.hostname Value copied directly
    remoteCountryCode
    		 principal.location.country_or_region Value copied directly
    serverHostname
    		 target.hostname Value copied directly
    serverName
    		 target.asset.hostname Value copied directly
    method
    		 network.http.method Value copied directly
    protocol
    		 network.application_protocol Value copied directly
    path
    		 target.url Value copied directly
    uri
    		 network.http.referral_url Value copied directly
    userAgent
    		 network.http.user_agent Value copied directly
    responseCode
    		 network.http.response_code Converted to integer
    responseSize
    		 network.received_bytes Converted to unsigned integer
    responseMillis
    		 additional.fields Mapped as response_millis label
    tags
    		 security_result.category_details Array of tag objects mapped to categories
    headersIn
    		 additional.fields Request headers mapped as key-value pairs
    headersOut
    		 additional.fields Response headers mapped as key-value pairs
    created
    		metadata.timestamp Mapped from changelog
    eventType
    		metadata.product_event_type Mapped from changelog
    message_data
    		metadata.description Mapped from changelog
    username
    		target.user.user_display_name Mapped from changelog
    userid
    		target.user.userid Mapped from changelog
    attachments.Fields.Title" , "attachments.Fields.Value
    		metadata.ingestion_labels Mapped from changelog
    msgData.detailLink
    		network.http.referral_url Mapped from changelog
    msgData.name
    		target.resource.name Mapped from changelog
    msgData.changes
    		target.resource.attribute.labels Mapped from changelog
    msgData.reason
    		security_result.summary Mapped from changelog
    msgData.conditions
    		security_result.description Mapped from changelog
    msgData.sites
    		network.http.user_agent Mapped from changelog

    Change Log

    View the Change Log for this parser

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