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

Supported in:

Google secops SIEM

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

Go to the Google Cloud Console.
Select your project or create a new one.
In the navigation menu, go to Cloud Storage > Buckets.
Click Create bucket.

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

Click Create.

Collect Fastly Next-Gen WAF API credentials

Get API access token

Sign in to the Fastly Next-Gen WAF dashboard.
Click your username in the upper-right corner, then select My Profile.
Go to API Access Tokens.
Click Add API access token.
Enter a descriptive name for the token (for example, Google SecOps Integration).
Click Create API access token.
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
Note: The API token is displayed only once at the time of creation. If you lose it, you must create a new token.

Get corp and site names

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

Verify permissions

To verify the account has the required permissions:

Sign in to the Fastly Next-Gen WAF dashboard.
Go to Manage > Corp > Corp Users.
Find your user account in the list.
Verify that your role is Admin, Owner, or Observer. These roles have the required API access to retrieve request feed data.
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}"

Create service account for Cloud Run function

The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.

Create service account

In the GCP Console, go to IAM & Admin > Service Accounts.
Click Create Service Account.
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
Click Create and Continue.
In the Grant this service account access to project section, 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.
Click Continue.
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:

Go to Cloud Storage > Buckets.
Click on your bucket name (for example, sigsci-waf-logs).
Go to the Permissions tab.
Click Grant access.
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
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.

In the GCP Console, go to Pub/Sub > Topics.
Click Create topic.
Provide the following configuration details:
- Topic ID: Enter sigsci-waf-logs-trigger
- Leave other settings as default
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.

In the GCP Console, go to Cloud Run.
Click Create service.
Select Function (use an inline editor to create a function).
In the Configure section, 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 3.12 or later
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.
In the Authentication section:
1. Select Require authentication.
2. Check Identity and Access Management (IAM).
Note: Pub/Sub will automatically handle authentication when invoking the function.
Scroll down and expand Containers, Networking, Security.
Go to the Security tab:
- Service account: Select the service account sigsci-waf-collector-sa.

Setting	Value
Service name	`sigsci-waf-collector`
Region	Select region matching your GCS bucket (for example, `us-central1`)
Runtime	Select Python 3.12 or later

Go to the Containers tab:

Click Variables & Secrets.
Click + Add variable for 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

In the Variables & Secrets section, scroll down to Requests:
- Request timeout: Enter 600 seconds (10 minutes)
Go to the Settings tab:
- In the Resources section:
  - Memory: Select 512 MiB or higher
  - CPU: Select 1
In the Revision scaling section:
- Minimum number of instances: Enter 0
- Maximum number of instances: Enter 100 (or adjust based on expected load)
Click Create.
Wait for the service to be created (1-2 minutes).
After the service is created, the inline code editor will open automatically.

Add function code

Enter main in the Entry point field.

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

Click Deploy to save and deploy the function.
Wait for deployment to complete (2-3 minutes).

Note: The Pub/Sub trigger configuration automatically creates the necessary subscriptions and permissions.

Create Cloud Scheduler job

Cloud Scheduler will publish messages to the Pub/Sub topic at regular intervals, triggering the Cloud Run function.

In the GCP Console, go to Cloud Scheduler.
Click Create Job.

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)

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

In the Cloud Scheduler console, find your job.
Click Force run to trigger the job manually.
Wait a few seconds.
Go to Cloud Run > Services.
Click on sigsci-waf-collector.
Click the Logs tab.

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

Go to Cloud Storage > Buckets.
Click on your bucket name (sigsci-waf-logs).
Navigate to the sigsci-waf/ folder.
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

Go to SIEM Settings > Feeds.
Click Add New Feed.
Click Configure a single feed.
In the Feed name field, enter a name for the feed (for example, Signal Sciences WAF Logs).
Select Google Cloud Storage V2 as the Source type.
Select Signal Sciences WAF as the Log type.
Click Get Service Account. A unique service account email will be displayed, for example:
```
chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com
```
Copy this email address.

Note: Each Google SecOps instance has a unique service account. Do not use service accounts from other documentation or examples.
Click Next.
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).
Note: Always include the trailing slash (/) at the end of the URI.
- Source deletion option: Select the deletion option according to your preference:
  - Never: Never deletes any files after transfers (recommended for testing).
  - Delete transferred files: Deletes files after successful transfer.
  - Delete transferred files and empty directories: Deletes files and empty directories after successful transfer.
    
    Note: If you select a deletion option, the service account must have Storage Object Admin role instead of Storage Object Viewer. Update IAM permissions accordingly.
- Maximum File Age: Include files modified in the last number of days (default is 180 days)
- Asset namespace: The asset namespace
- Ingestion labels: The label to be applied to the events from this feed
Click Next.
Review your new feed configuration in the Finalize screen, and then click Submit.

Grant IAM permissions to the Google SecOps service account

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

Go to Cloud Storage > Buckets.
Click on your bucket name.
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Paste the Google SecOps service account email
- Assign roles: Select Storage Object Viewer
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

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