Collect IBM Security Verify SaaS logs

Supported in:

Google secops SIEM

This document explains how to ingest IBM Security Verify SaaS logs to Google Security Operations using Google Cloud Storage V2.

IBM Security Verify SaaS is a cloud-based identity and access management platform that provides SSO, MFA, and adaptive access controls. It generates audit logs for authentication events, policy decisions, and user lifecycle management.

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 IBM Security Verify SaaS (administrator role)

Create a 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, `ibm-verify-saas-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 IBM Security Verify SaaS API credentials

Create API client

Sign in to the IBM Security Verify admin console.
Go to Security > API Access.
Click Add API Client.
Enter a name for the API client (for example, Google Security Operations Integration).
Assign the following permissions:
- Read event logs
- Read reports
Click Save.
Copy and save the following details in a secure location:
- Client ID: The API client identifier.
- Client Secret: The API client secret key.

Determine tenant URL

The API base URL is derived from your IBM Security Verify tenant. The format is:

Console URL	API Base URL
`https://YOUR_TENANT.verify.ibm.com`	`https://YOUR_TENANT.verify.ibm.com`

Replace YOUR_TENANT with your actual IBM Security Verify tenant name.

Verify permissions

To verify the API client has the required permissions:

Sign in to the IBM Security Verify admin console.
Go to Security > API Access.
Click on the API client name.
Verify that Read event logs and Read reports permissions are enabled.
If permissions are missing, contact your IBM Security Verify administrator.

Test API access

Test your credentials before proceeding with the integration:

# Replace with your actual credentials
CLIENT_ID="your-client-id"
CLIENT_SECRET="your-client-secret"
TENANT_URL="https://YOUR_TENANT.verify.ibm.com"

# Get OAuth token
TOKEN=$(curl -s -X POST "${TENANT_URL}/v1.0/endpoint/default/token" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials&client_id=${CLIENT_ID}&client_secret=${CLIENT_SECRET}" \
  | jq -r '.access_token')

# Test API access
curl -v -H "Authorization: Bearer ${TOKEN}" "${TENANT_URL}/v1.0/events?size=1"

Create a 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 the 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 ibm-verify-collector-sa.
- Service account description: Enter Service account for Cloud Run function to collect IBM Security Verify SaaS 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 your bucket name (for example, ibm-verify-saas-logs).
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Enter the service account email (for example, ibm-verify-collector-sa@your-project.iam.gserviceaccount.com).
- Assign roles: Select Storage Object Admin.
Click Save.

Create a 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 ibm-verify-trigger.
- Leave other settings as default.
Click Create.

Create a Cloud Run function to collect logs

The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from IBM Security Verify SaaS API and write them 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 ibm-verify-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 ibm-verify-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 ibm-verify-collector-sa.

Setting	Value
Service name	`ibm-verify-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`	`ibm-verify-saas-logs`	GCS bucket name
`GCS_PREFIX`	`ibm-verify`	Prefix for log files
`STATE_KEY`	`ibm-verify/state.json`	State file path
`TENANT_URL`	`https://YOUR_TENANT.verify.ibm.com`	IBM Security Verify tenant URL
`CLIENT_ID`	`your-client-id`	API client ID
`CLIENT_SECRET`	`your-client-secret`	API client secret
`MAX_RECORDS`	`10000`	Max records per run
`PAGE_SIZE`	`1000`	Records per page
`LOOKBACK_HOURS`	`24`	Initial lookback period

Scroll down in the Variables & Secrets tab to Requests:
- Request timeout: Enter 600 seconds (10 minutes).
Go to the Settings tab in Containers:
- 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 Function entry point.

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', 'ibm-verify')
STATE_KEY = os.environ.get('STATE_KEY', 'ibm-verify/state.json')
TENANT_URL = os.environ.get('TENANT_URL', '').rstrip('/')
CLIENT_ID = os.environ.get('CLIENT_ID', '')
CLIENT_SECRET = os.environ.get('CLIENT_SECRET', '')
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'))

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)

def get_token():
  """Get OAuth 2.0 access token using client credentials flow."""
  token_url = f"{TENANT_URL}/v1.0/endpoint/default/token"

  body = f"grant_type=client_credentials&client_id={CLIENT_ID}&client_secret={CLIENT_SECRET}"

  headers = {
    'Content-Type': 'application/x-www-form-urlencoded',
    'Accept': 'application/json'
  }

  backoff = 1.0
  max_retries = 3

  for attempt in range(max_retries):
    response = http.request('POST', token_url, body=body.encode('utf-8'), headers=headers)

    if response.status == 429:
      retry_after = int(response.headers.get('Retry-After', str(int(backoff))))
      print(f"Rate limited (429) on token request. Retrying after {retry_after}s...")
      time.sleep(retry_after)
      backoff = min(backoff * 2, 30.0)
      continue

    if response.status != 200:
      raise RuntimeError(f"Failed to get access token: {response.status} - {response.data.decode('utf-8')}")

    data = json.loads(response.data.decode('utf-8'))
    return data['access_token']

  raise RuntimeError(f"Failed to get token after {max_retries} retries due to rate limiting")

@functions_framework.cloud_event
def main(cloud_event):
  """
  Cloud Run function triggered by Pub/Sub to fetch IBM Security Verify SaaS logs and write to GCS.

  Args:
    cloud_event: CloudEvent object containing Pub/Sub message
  """

  if not all([GCS_BUCKET, TENANT_URL, CLIENT_ID, CLIENT_SECRET]):
    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"])
        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()}")

    # Get access token
    token = get_token()

    # Fetch logs
    records, newest_event_time = fetch_logs(
      token=token,
      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}")

    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(token: str, start_time: datetime, end_time: datetime, page_size: int, max_records: int):
  """
  Fetch logs from IBM Security Verify SaaS Events API with pagination and rate limiting.

  Args:
    token: OAuth 2.0 access token
    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)
  """
  endpoint = f"{TENANT_URL}/v1.0/events"

  headers = {
    'Authorization': f'Bearer {token}',
    'Accept': 'application/json',
    'User-Agent': 'GoogleSecOps-IBMVerifyCollector/1.0'
  }

  records = []
  newest_time = None
  page_num = 0
  backoff = 1.0

  start_iso = start_time.strftime('%Y-%m-%dT%H:%M:%S.000Z')
  end_iso = end_time.strftime('%Y-%m-%dT%H:%M:%S.000Z')

  # IBM Verify Events API uses filter and sort_order with pagination
  search_after = None

  while True:
    page_num += 1

    if len(records) >= max_records:
      print(f"Reached max_records limit ({max_records})")
      break

    params = []
    params.append(f"size={min(page_size, max_records - len(records))}")
    params.append(f"filter=time+ge+\"{start_iso}\"+and+time+le+\"{end_iso}\"")
    params.append("sort_order=asc")
    if search_after:
      params.append(f"search_after={search_after}")

    url = f"{endpoint}?{'&'.join(params)}"

    try:
      response = http.request('GET', url, headers=headers)

      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('events', [])

      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_time = event.get('time')
          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 using search_after pagination
      if len(page_results) < page_size:
        print(f"Reached last page (size={len(page_results)} < limit={page_size})")
        break

      # Use the last event's sort value for pagination
      last_event = page_results[-1]
      search_after = last_event.get('time', '')

    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

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 a 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	`ibm-verify-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 `ibm-verify-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 (ibm-verify-collector-hourly).
Click Force run to trigger manually.
Wait a few seconds and go to Cloud Run > Services > ibm-verify-collector > Logs.

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://ibm-verify-saas-logs/ibm-verify/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

Check the GCS bucket (ibm-verify-saas-logs) to confirm logs were written.

If you see errors in the logs:

HTTP 401: Check API credentials in environment variables
HTTP 403: Verify API client has required permissions in IBM Security Verify admin console
HTTP 429: Rate limiting - function will automatically retry with backoff
Failed to get access token: Verify TENANT_URL, CLIENT_ID, and CLIENT_SECRET are correct

Configure a feed in Google SecOps to ingest IBM Security Verify SaaS 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, IBM Security Verify SaaS Logs).
Select Google Cloud Storage V2 as the Source type.
Select IBM Security Verify SaaS 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. You will use it in the next step.

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://ibm-verify-saas-logs/ibm-verify/
```
  - Replace:
    - ibm-verify-saas-logs: Your GCS bucket name.
    - ibm-verify: 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 your bucket name (ibm-verify-saas-logs).
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.

Note: If you plan to use the deletion option "Delete transferred files" or "Delete transferred files and empty directories," grant Storage Object Admin role instead of Storage Object Viewer.

UDM mapping table

Log Field	UDM Mapping	Logic
data.realm, geoip.continent_name, data.applicationtype, data.id_token, data.grant_id, data.grant_type, data.at_hash, data.rt_hash, id, data.scope, data.uasessionid, data.performedby_type, data.subject_type, data.target_type, data.templateid, data.subjectid, data.performedby, data.action, data.performedby_clientname, geoip.as_org, geoip.country_iso_code, operation.op, operation.path, operation.value, operation.value.name, operation.value.values	additional.fields	Additional metadata fields not covered by standard UDM fields
auth_type	extensions.auth.type	Type of authentication used
meta_event_type	metadata.event_type	Type of event (e.g., USER_LOGIN, NETWORK_CONNECTION)
tenantid	metadata.product_deployment_id	Identifier for the product deployment
event_type	metadata.product_event_type	Product-specific event type
data.ib_request_id	metadata.product_log_id	Product-specific log identifier
data.devicetype	network.http.parsed_user_agent	Parsed user agent string
data.devicetype	network.http.user_agent	User agent string
data.sessionid	network.session_id	Session identifier
tenantname	observer.hostname	Hostname of the observer
data.applicationname	principal.application	Application associated with the principal
data.origin, geoip.ip	principal.ip	IP address of the principal
geoip.city_name	principal.location.city	City of the principal's location
geoip.country_name	principal.location.country_or_region	Country or region of the principal's location
geoip.location.lat	principal.location.region_latitude	Latitude of the principal's location
geoip.location.lon	principal.location.region_longitude	Longitude of the principal's location
geoip.region_name	principal.location.state	State of the principal's location
data.applicationid	principal.resource.product_object_id	Product object identifier for the resource
data.subtype	principal.resource.resource_subtype	Subtype of the resource
data.redirecturl	principal.url	URL associated with the principal
data.username	principal.user.user_display_name	Display name of the user
data.userid	principal.user.userid	User identifier
security_result	security_result	Security result information
data.client_id, data.client_name, data.client_type	security_result.about.resource.attribute.labels	Labels for resource attributes
action1	security_result.action	Action taken by the security system
data.result	security_result.action_details	Details of the security action
data.status_code, correlationid	security_result.detection_fields	Fields related to detection
servicename	target.application	Application targeted
data.targetid	target.resource.id	Identifier of the target resource
data.account_name	target.user.user_display_name	Display name of the target user
data.target	target.user.userid	User identifier of the target
	metadata.product_name	Product name
	metadata.vendor_name	Vendor/company name

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