Collect Alveo Risk Data Management logs

Parser Version: 1.0

Supported in:

This document explains how to ingest Alveo Risk Data Management (RDM) logs to Google Security Operations using Google Cloud Storage V2.

Alveo RDM, part of the Prime EDM platform by Gresham Technologies, is a cloud-based financial data management solution that helps banks, asset managers, insurers, and other financial institutions manage market data, risk factors, and reference data across multiple asset classes. The platform provides a RESTful API through its Ops360 interface for programmatic access to data operations, audit trail records, and data lineage information. These audit and operational logs can be exported through the REST API and written to a GCS bucket for ingestion by Google SecOps.

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 create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
  • An Alveo RDM (Prime EDM) instance with REST API access enabled
  • An Alveo user account with API credentials and permissions to access audit trail data
  • API client credentials (Client ID and Client Secret) generated from the Alveo administration console

Collect Alveo RDM API credentials

Get the Alveo instance details

  1. Sign in to your Alveo RDM (Prime EDM) instance.

  2. Note the following values from the browser address bar:

    • Base URL: The API server address (for example, https://yourcompany.alveotech.com)
    • Tenant ID: Your organization's tenant identifier, displayed in the administration console

Create API credentials

  1. Sign in to the Alveo RDM administration console.
  2. Go to Administration > API Access > Client Credentials.
  3. Click Create New Client.
  4. Provide the following configuration details:
    • Client Name: Enter a descriptive name (for example, Google SecOps Integration)
    • Scopes: Select audit:read and data:read
  5. Click Create.

  6. Copy and securely store the generated credentials:

    • Client ID: Copy this value
    • Client Secret: Copy this value

Verify permissions

To verify the account has the required permissions:

  1. Sign in to the Alveo RDM console with API user credentials.
  2. Go to Administration > Audit Trail.
  3. Verify that audit trail records are visible and accessible.
  4. If you cannot see the Audit Trail section, contact your Alveo administrator to grant the necessary permissions.

Test API access

  • Test your credentials before proceeding with the integration:

    ALVEO_BASE_URL="https://yourcompany.alveotech.com"
ALVEO_CLIENT_ID="your-client-id"
ALVEO_CLIENT_SECRET="your-client-secret"

# Get OAuth2 access token
TOKEN=$(curl -s -X POST "${ALVEO_BASE_URL}/oauth/token" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials&client_id=${ALVEO_CLIENT_ID}&client_secret=${ALVEO_CLIENT_SECRET}" \
  | python3 -c "import sys,json; print(json.load(sys.stdin).get('access_token',''))")

echo "Token: ${TOKEN}"

# Test audit trail API access
curl -v "${ALVEO_BASE_URL}/api/v1/audit/events?limit=1" \
  -H "Authorization: Bearer ${TOKEN}" \
  -H "Accept: application/json"

Create a 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, alveo-rdm-audit-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.

Create a service account for the 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

  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 alveo-rdm-logs-collector-sa
    • Service account description: Enter Service account for Cloud Run function to collect Alveo RDM audit logs
  4. Click Create and Continue.
  5. 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.
  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 the GCS bucket

Grant the service account write permissions on the GCS bucket:

  1. Go to Cloud Storage > Buckets.
  2. Click your bucket name.
  3. Go to the Permissions tab.
  4. Click Grant access.
  5. Provide the following configuration details:
    • Add principals: Enter the service account email (for example, alveo-rdm-logs-collector-sa@PROJECT_ID.iam.gserviceaccount.com)
    • Assign roles: Select Storage Object Admin
  6. 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.

  1. In the GCP Console, go to Pub/Sub > Topics.
  2. Click Create topic.
  3. Provide the following configuration details:
    • Topic ID: Enter alveo-rdm-logs-trigger
    • Leave other settings as default
  4. 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 audit trail logs from the Alveo RDM REST 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 Configure section, provide the following configuration details:

    Setting Value
    Service name alveo-rdm-logs-collector
    Region Select region matching your GCS bucket (for example, us-central1)
    Runtime Select Python 3.12 or 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 alveo-rdm-logs-trigger.
    4. Click Save.

  6. In the Authentication section:

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

  7. Scroll down and expand Containers, Networking, Security.

  8. Go to the Security tab:

    • Service account: Select the service account alveo-rdm-logs-collector-sa

  9. Go to the Containers tab:

    1. Click Variables & Secrets.
    2. Click + Add variable for each environment variable:
    Variable Name Example Value Description
    GCS_BUCKET alveo-rdm-audit-logs GCS bucket name
    GCS_PREFIX alveo-rdm Prefix for log files
    STATE_KEY alveo-rdm/state.json State file path
    ALVEO_BASE_URL https://yourcompany.alveotech.com Alveo RDM API base URL
    ALVEO_CLIENT_ID your-client-id OAuth2 Client ID
    ALVEO_CLIENT_SECRET your-client-secret OAuth2 Client Secret
    PAGE_SIZE 100 Records per API page
    MAX_RECORDS 10000 Max records per run
    LOOKBACK_HOURS 2 Initial lookback period

  10. Scroll down in the Variables & Secrets section to Requests:

    • Request timeout: Enter 600 seconds (10 minutes)

  11. Go to the Settings tab:

    • In the Resources section:
      • Memory: Select 512 MiB or higher
      • CPU: Select 1

  12. In the Revision scaling section:

    • 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 editor will open automatically.

Add function code

  1. Enter main in the Entry point field.

  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
import urllib.parse
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', 'alveo-rdm').strip('/')
STATE_KEY = os.environ.get('STATE_KEY') or f"{GCS_PREFIX}/state.json"
ALVEO_BASE_URL = os.environ.get('ALVEO_BASE_URL', '').rstrip('/')
ALVEO_CLIENT_ID = os.environ.get('ALVEO_CLIENT_ID')
ALVEO_CLIENT_SECRET = os.environ.get('ALVEO_CLIENT_SECRET')
PAGE_SIZE = int(os.environ.get('PAGE_SIZE', '100'))
MAX_RECORDS = int(os.environ.get('MAX_RECORDS', '10000'))
LOOKBACK_HOURS = int(os.environ.get('LOOKBACK_HOURS', '2'))

def parse_datetime(value):
  """Parse ISO datetime string to datetime object."""
  if not value:
    return None
  if value.endswith("Z"):
    value = value[:-1] + "+00:00"
  try:
    return datetime.fromisoformat(value)
  except Exception:
    return None

def get_access_token():
  """Get OAuth2 access token using client credentials."""
  token_url = f"{ALVEO_BASE_URL}/oauth/token"
  body = urllib.parse.urlencode({
    'grant_type': 'client_credentials',
    'client_id': ALVEO_CLIENT_ID,
    'client_secret': ALVEO_CLIENT_SECRET,
  }).encode('utf-8')

  response = http.request(
    'POST',
    token_url,
    body=body,
    headers={'Content-Type': 'application/x-www-form-urlencoded'},
  )

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

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

@functions_framework.cloud_event
def main(cloud_event):
  """
  Cloud Run function triggered by Pub/Sub to fetch
  Alveo RDM audit trail records and write to GCS.

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

  if not all([GCS_BUCKET, ALVEO_BASE_URL, ALVEO_CLIENT_ID,
        ALVEO_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"])
        # Overlap by 2 minutes to catch delayed events
        if last_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 audit logs from {last_time.isoformat()} "
      f"to {now.isoformat()}"
    )

    # Get OAuth2 token
    token = get_access_token()
    print("Successfully obtained OAuth2 access token")

    # Fetch audit logs
    records, newest_event_time = fetch_audit_logs(
      token=token,
      start_time=last_time,
      end_time=now,
    )

    if not records:
      print("No new audit 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 "
      f"gs://{GCS_BUCKET}/{object_key}"
    )

    # Update state
    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 audit 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):
  """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_audit_logs(token, start_time, end_time):
  """
  Fetch audit trail logs from Alveo RDM REST API with pagination.

  Args:
    token: OAuth2 access token
    start_time: Start time for log query
    end_time: End time for log query

  Returns:
    Tuple of (records list, newest_event_time ISO string)
  """
  endpoint = f"{ALVEO_BASE_URL}/api/v1/audit/events"

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

  start_iso = start_time.isoformat()
  end_iso = end_time.isoformat()

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

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

    params = (
      f"?startTime={urllib.parse.quote(start_iso)}"
      f"&endTime={urllib.parse.quote(end_iso)}"
      f"&offset={offset}"
      f"&limit={min(PAGE_SIZE, MAX_RECORDS - len(records))}"
    )
    url = f"{endpoint}{params}"

    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('events', [])

      if not page_results:
        print(f"No more results (empty page {page_num})")
        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('timestamp')
          if event_time:
            if newest_time is None:
              newest_time = event_time
            else:
              current_dt = parse_datetime(event_time)
              newest_dt = parse_datetime(newest_time)
              if (current_dt and newest_dt
                  and current_dt > newest_dt):
                newest_time = event_time
        except Exception as e:
          print(
            f"Warning: Could not parse event time: {e}"
          )

      # Check pagination
      if len(page_results) < PAGE_SIZE:
        print(
          f"Reached last page "
          f"(size={len(page_results)} < limit={PAGE_SIZE})"
        )
        break

      offset += len(page_results)
      page_num += 1

    except Exception as e:
      print(f"Error fetching audit logs: {e}")
      return [], None

  print(
    f"Retrieved {len(records)} total records "
    f"from {page_num + 1} pages"
  )
  return records, newest_time

    • Second file - requirements.txt:

      functions-framework==3.*
google-cloud-storage==2.*
urllib3>=2.0.0

  3. Click Deploy to save and deploy the function.

  4. Wait for deployment to complete (2-3 minutes).

Create a 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 alveo-rdm-logs-collector-hourly
    Region Select same region as Cloud Run function
    Frequency 0 * * * * (every hour, on the hour)
    Timezone Select timezone (UTC recommended)
    Target type Pub/Sub
    Topic Select the topic alveo-rdm-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 hour 0 * * * * Standard (recommended)
Every 2 hours 0 */2 * * * Lower volume
Every 6 hours 0 */6 * * * Low volume, batch processing

Test the integration

  1. In the Cloud Scheduler console, find your job.
  2. Click Force run to trigger the job manually.
  3. Wait a few seconds.
  4. Go to Cloud Run > Services.
  5. Click on the function name alveo-rdm-logs-collector.
  6. Click the Logs tab.

  7. Verify the function executed successfully. Look for:

    Successfully obtained OAuth2 access token
Fetching audit logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Page 0: Retrieved X events
Wrote X records to gs://alveo-rdm-audit-logs/alveo-rdm/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

  8. Go to Cloud Storage > Buckets.

  9. Click your bucket name.

  10. Navigate to the prefix folder alveo-rdm/.

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

If you see errors in the logs:

  • HTTP 401: Check OAuth2 credentials in environment variables. Verify Client ID and Client Secret are correct.
  • HTTP 403: Verify the API client has the required scopes (audit:read, data:read).
  • HTTP 429: Rate limiting - function will automatically retry with backoff.
  • Token error: Check that the ALVEO_BASE_URL is correct and the OAuth2 token endpoint is reachable.
  • Missing environment variables: Check all required variables are set.

Configure a feed in Google SecOps to ingest Alveo RDM logs

  1. Go to SIEM Settings > Feeds.
  2. Click Add New Feed.
  3. Click Configure a single feed.
  4. In the Feed name field, enter a name for the feed (for example, Alveo RDM Audit Logs).
  5. Select Google Cloud Storage V2 as the Source type.
  6. Select Alveo Risk Data Management as 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 for use in the next step.

  9. Click Next.

  10. Specify values for the following input parameters:

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

      gs://alveo-rdm-audit-logs/alveo-rdm/
      • Replace:
        • alveo-rdm-audit-logs: Your GCS bucket name.
        • alveo-rdm: 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 Finalize screen, and then click Submit.

Grant IAM permissions to the Google SecOps service account

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

  1. Go to Cloud Storage > Buckets.
  2. Click your bucket name.
  3. Go to the Permissions tab.
  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
function_label additional.fields Merged
interface_attribute_label additional.fields Merged
interface_datafile_label additional.fields Merged
log_level_label additional.fields Merged
new_value_label additional.fields Merged
object_label additional.fields Merged
old_value_label additional.fields Merged
operation_type_label additional.fields Merged
register_type_label additional.fields Merged
status_process_label additional.fields Merged
msg extensions.auth.type Mapped: logon → MACHINE
date metadata.event_timestamp Parsed as MMM dd yyyy HH:mm:ss
date_hours metadata.event_timestamp Parsed as yyyyMMdd HHmmss
has_principal metadata.event_type Mapped: true → NETWORK_CONNECTION, true → STATUS_UPDATE
has_user metadata.event_type Mapped: true → USER_UNCATEGORIZED
msg metadata.event_type Mapped: logon → USER_LOGIN
host_name principal.asset.hostname Directly mapped
src_ip principal.asset.ip Merged
host_name principal.hostname Directly mapped
src_ip principal.ip Merged
country principal.location.country_or_region Directly mapped
object_type principal.user.role_name Directly mapped
modified_user principal.user.userid Directly mapped
act security_result.action Merged
msg security_result.action Mapped: failed → act
msg security_result.description Directly mapped
process_id target.process.pid Directly mapped
affected_user target.user.userid Directly mapped
user_id target.user.userid Directly mapped
N/A extensions.auth.type Constant: MACHINE
N/A metadata.event_type Constant: USER_LOGIN
N/A metadata.product_name Constant: ALVEO RDM
N/A metadata.vendor_name Constant: ALVEO RDM

Change Log

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