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Collect Asset Panda logs

Parser Version: 1.0

Supported in:

Google secops SIEM

This document explains how to ingest Asset Panda logs to Google Security Operations using Google Cloud Storage V2.

Asset Panda is a cloud-based asset management platform that enables organizations to track, manage, and report on physical and digital assets throughout their lifecycle. The Asset Panda REST API provides access to change logs that record field-level modifications, asset transfers, action history, and user activity across all asset groups. These change logs can be collected through the 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 Asset Panda account with administrator access (required for API configuration)
An Asset Panda API key and secret with read permissions

Generate Asset Panda API credentials

Navigate to API Configuration

Sign in to your Asset Panda account as an administrator.
Click the Settings (gear) icon in the top-right corner.
Select API Configuration.

Create an API key

On the API Configuration page, click Create New API Key.
Provide the following configuration details:
- Name: Enter a descriptive name for the key (for example, SIEM Change Log Collector)
- Permissions: Select Read
Note: For security, grant only read permissions. Write and delete permissions are not required for log collection.
Click Save.
Record the following credentials displayed at the bottom of the page:
- API Key (also called Client ID)
- API Secret (also called Client Secret)
Note: The API secret is displayed only once. Store both values securely. If you lose the secret, you must generate a new API key.

Identify group IDs

The Cloud Run function needs group IDs to query objects and their change logs. To find your group IDs:

In Asset Panda, go to Settings > Group Settings.
Click the Edit link next to a group name.
Note the numeric value near the end of the browser URL. This is the group ID (for example, 12345).
Repeat for each group whose change logs you want to collect.

Test API access

Test your credentials before proceeding with the integration:

API_KEY="your-api-key"
API_SECRET="your-api-secret"

# Test authentication by retrieving account settings
curl -s "https://api.assetpanda.com/v3/settings" \
  -H "Accept: application/json" \
  -H "Access-Key-Id: ${API_KEY}" \
  -H "Access-Key-Secret: ${API_SECRET}"

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, `asset-panda-change-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.

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

In the GCP Console, go to IAM & Admin > Service Accounts.
Click Create Service Account.
Provide the following configuration details:
- Service account name: Enter asset-panda-logs-collector-sa
- Service account description: Enter Service account for Cloud Run function to collect Asset Panda change 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 the GCS bucket

Grant the service account write permissions on the GCS bucket:

Go to Cloud Storage > Buckets.
Click your bucket name.
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Enter the service account email (for example, asset-panda-logs-collector-sa@PROJECT_ID.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 asset-panda-logs-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 change logs from the Asset Panda REST 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 asset-panda-logs-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 asset-panda-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 asset-panda-logs-collector-sa

Setting	Value
Service name	`asset-panda-logs-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`	`asset-panda-change-logs`	GCS bucket name
`GCS_PREFIX`	`asset-panda`	Prefix for log files
`STATE_KEY`	`asset-panda/state.json`	State file path
`AP_API_KEY`	`your-api-key`	Asset Panda API key
`AP_API_SECRET`	`your-api-secret`	Asset Panda API secret
`AP_GROUP_IDS`	`12345,67890`	Comma-separated group IDs to monitor
`PAGE_SIZE`	`50`	Records per API page (max 50)
`MAX_RECORDS`	`10000`	Max records per run
`LOOKBACK_HOURS`	`2`	Initial lookback period

Scroll down in the Variables & Secrets section 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:

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

# 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', 'asset-panda').strip('/')
STATE_KEY = os.environ.get('STATE_KEY') or f"{GCS_PREFIX}/state.json"
AP_API_KEY = os.environ.get('AP_API_KEY')
AP_API_SECRET = os.environ.get('AP_API_SECRET')
AP_GROUP_IDS = os.environ.get('AP_GROUP_IDS', '')
PAGE_SIZE = int(os.environ.get('PAGE_SIZE', '50'))
MAX_RECORDS = int(os.environ.get('MAX_RECORDS', '10000'))
LOOKBACK_HOURS = int(os.environ.get('LOOKBACK_HOURS', '2'))

BASE_URL = 'https://api.assetpanda.com'

# Rate limiting: 400 calls per 3 minutes
RATE_LIMIT_CALLS = 400
RATE_LIMIT_WINDOW = 180  # seconds
call_timestamps = []

def rate_limit_wait():
  """Enforce rate limiting of 400 calls per 3-minute window."""
  global call_timestamps
  now = time.time()
  call_timestamps = [t for t in call_timestamps if now - t < RATE_LIMIT_WINDOW]
  if len(call_timestamps) >= RATE_LIMIT_CALLS:
    sleep_time = RATE_LIMIT_WINDOW - (now - call_timestamps[0]) + 1
    print(f"Rate limit approaching. Sleeping {sleep_time:.1f}s...")
    time.sleep(sleep_time)
    call_timestamps = [t for t in call_timestamps if time.time() - t < RATE_LIMIT_WINDOW]
  call_timestamps.append(time.time())

def api_request(method, path, params=None):
  """Make an authenticated request to the Asset Panda API."""
  rate_limit_wait()

  url = f"{BASE_URL}{path}"
  headers = {
    'Accept': 'application/json',
    'Content-Type': 'application/json',
    'Access-Key-Id': AP_API_KEY,
    'Access-Key-Secret': AP_API_SECRET,
  }

  backoff = 1.0
  for attempt in range(3):
    try:
      if method == 'GET':
        response = http.request(method, url, headers=headers, fields=params)
      else:
        body = json.dumps(params or {}).encode('utf-8')
        response = http.request(method, url, headers=headers, body=body)

      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, 60.0)
        continue

      if response.status == 200:
        return json.loads(response.data.decode('utf-8'))

      print(f"HTTP {response.status}: {response.data.decode('utf-8')[:500]}")
      return None

    except Exception as e:
      print(f"Request error (attempt {attempt + 1}): {e}")
      if attempt < 2:
        time.sleep(backoff)
        backoff *= 2

  return None

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

@functions_framework.cloud_event
def main(cloud_event):
  """
  Cloud Run function triggered by Pub/Sub to fetch
  Asset Panda change logs and write to GCS.

  Args:
    cloud_event: CloudEvent object containing Pub/Sub message
  """
  if not all([GCS_BUCKET, AP_API_KEY, AP_API_SECRET, AP_GROUP_IDS]):
    print('Error: Missing required environment variables')
    return

  group_ids = [gid.strip() for gid in AP_GROUP_IDS.split(',') if gid.strip()]
  if not group_ids:
    print('Error: No group IDs configured')
    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_run_time"):
      try:
        last_time = parse_datetime(state["last_run_time"])
        if last_time:
          last_time = last_time - timedelta(minutes=2)
      except Exception as e:
        print(f"Warning: Could not parse last_run_time: {e}")

    if last_time is None:
      last_time = now - timedelta(hours=LOOKBACK_HOURS)

    print(f"Collecting change logs from {last_time.isoformat()} to {now.isoformat()}")

    all_records = []

    for group_id in group_ids:
      if len(all_records) >= MAX_RECORDS:
        print(f"Reached max_records limit ({MAX_RECORDS})")
        break

      records = fetch_group_change_logs(group_id, last_time, now)
      all_records.extend(records)
      print(f"Group {group_id}: collected {len(records)} change log entries")

    if not all_records:
      print("No new change 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 all_records]
    ) + '\n'
    blob.upload_from_string(ndjson, content_type='application/x-ndjson')

    print(f"Wrote {len(all_records)} records to gs://{GCS_BUCKET}/{object_key}")

    # Update state
    save_state(bucket, STATE_KEY, now.isoformat())
    print(f"Successfully processed {len(all_records)} records")

  except Exception as e:
    print(f'Error processing change logs: {str(e)}')
    raise

def fetch_group_change_logs(group_id, start_time, end_time):
  """
  Fetch change logs for all objects in a group.

  Args:
    group_id: Asset Panda group ID
    start_time: Start time for log query
    end_time: End time for log query

  Returns:
    List of change log records
  """
  records = []
  offset = 0

  # Iterate through objects in the group
  while True:
    if len(records) >= MAX_RECORDS:
      break

    search_data = api_request(
      'POST',
      f'/v3/groups/{group_id}/search_objects',
      {'offset': offset, 'limit': PAGE_SIZE}
    )

    if not search_data:
      break

    objects = search_data.get('objects', [])
    if not objects:
      break

    for obj in objects:
      object_id = obj.get('id')
      if not object_id:
        continue

      change_logs = fetch_object_change_logs(
        object_id, group_id, start_time, end_time
      )
      records.extend(change_logs)

      if len(records) >= MAX_RECORDS:
        break

    total = search_data.get('totals', {}).get('objects', 0)
    offset += len(objects)
    if offset >= total:
      break

  return records

def fetch_object_change_logs(object_id, group_id, start_time, end_time):
  """
  Fetch change logs for a specific object.

  Args:
    object_id: Asset Panda object ID
    group_id: Asset Panda group ID
    start_time: Start time filter
    end_time: End time filter

  Returns:
    List of change log records with metadata
  """
  records = []
  offset = 0

  while True:
    data = api_request(
      'GET',
      f'/v3/entity_objects/{object_id}/change_logs',
      {'limit': PAGE_SIZE, 'offset': offset}
    )

    if not data:
      break

    logs = data.get('change_logs', data.get('data', []))
    if isinstance(logs, dict):
      logs = [logs]

    if not logs:
      break

    for log_entry in logs:
      log_time = parse_datetime(
        log_entry.get('created_at') or log_entry.get('updated_at', '')
      )

      if log_time and log_time < start_time:
        return records
      if log_time and log_time > end_time:
        continue

      log_entry['_group_id'] = group_id
      log_entry['_object_id'] = object_id
      records.append(log_entry)

    if len(logs) < PAGE_SIZE:
      break

    offset += len(logs)

  return records

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_run_time_iso):
  """Save the last run timestamp to GCS state file."""
  try:
    state = {'last_run_time': last_run_time_iso}
    blob = bucket.blob(key)
    blob.upload_from_string(
      json.dumps(state, indent=2),
      content_type='application/json'
    )
    print(f"Saved state: last_run_time={last_run_time_iso}")
  except Exception as e:
    print(f"Warning: Could not save state: {e}")

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. Asset Panda enforces a rate limit of 400 calls per 3-minute window per IP address. The function includes built-in rate limiting to respect this constraint.

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	`asset-panda-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 `asset-panda-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 hour	`0 * * * *`	Standard (recommended)
Every 2 hours	`0 /2 * *`	Lower volume
Every 6 hours	`0 /6 * *`	Low volume, batch processing

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 the function name asset-panda-logs-collector.
Click the Logs tab.

Verify the function executed successfully. Look for:

Collecting change logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Group 12345: collected X change log entries
Wrote X records to gs://asset-panda-change-logs/asset-panda/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

Go to Cloud Storage > Buckets.
Click your bucket name.
Navigate to the prefix folder asset-panda/.
Verify that a new .ndjson file was created with the current timestamp.

If you see errors in the logs:

HTTP 401: Check API credentials in environment variables. Verify the API key and secret are correct.
HTTP 403: Verify the API key has read permissions enabled.
HTTP 429: Rate limiting - function will automatically retry with backoff.
Empty results: Verify that the configured group IDs exist and contain objects with change history.
Missing environment variables: Check all required variables are set.

Configure a feed in Google SecOps to ingest Asset Panda 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, Asset Panda Change Logs).
Select Google Cloud Storage V2 as the Source type.
Select Asset Panda 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 for use in the next step.

Note: Each Google SecOps instance has a unique service account. Don't 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://asset-panda-change-logs/asset-panda/
```
  - Replace:
    - asset-panda-change-logs: Your GCS bucket name.
    - asset-panda: 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.
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
`array_values_label`	`additional.fields`	Merged
`data_label`	`additional.fields`	Merged
`field_values_label`	`additional.fields`	Merged
`low_values_label`	`additional.fields`	Merged
`value_ids_label`	`additional.fields`	Merged
`values_label`	`additional.fields`	Merged
`display_with_secondary`	`intermediary.user.user_display_name`	Directly mapped
`secondary_name`	`intermediary.user.userid`	Directly mapped
`created_at`	`metadata.event_timestamp`	Parsed as `RFC 3339`
`has_user`	`metadata.event_type`	Mapped: `true` → `USER_UNCATEGORIZED`
`object_version_ids`	`metadata.product_version`	Directly mapped
`share_url`	`metadata.url_back_to_product`	Directly mapped
`display_name`	`principal.user.user_display_name`	Directly mapped
`id`	`principal.user.userid`	Directly mapped
`account_id_label`	`security_result.detection_fields`	Merged
`change_source_label`	`security_result.detection_fields`	Merged
`change_trigger_label`	`security_result.detection_fields`	Merged
`created_at_label`	`security_result.detection_fields`	Merged
`date_format_label`	`security_result.detection_fields`	Merged
`docusign_envelope_id_label`	`security_result.detection_fields`	Merged
`embedded_into_object_id_label`	`security_result.detection_fields`	Merged
`entity_action_id_label`	`security_result.detection_fields`	Merged
`entity_id_label`	`security_result.detection_fields`	Merged
`entity_id_label1`	`security_result.detection_fields`	Merged
`entity_key_label`	`security_result.detection_fields`	Merged
`google_calendar_sync_label`	`security_result.detection_fields`	Merged
`gps_coordinates_label`	`security_result.detection_fields`	Merged
`has_audit_history_label`	`security_result.detection_fields`	Merged
`id_label`	`security_result.detection_fields`	Merged
`is_archived_label`	`security_result.detection_fields`	Merged
`is_deletable_label`	`security_result.detection_fields`	Merged
`is_editable_label`	`security_result.detection_fields`	Merged
`is_locked_label`	`security_result.detection_fields`	Merged
`linked_action_object_id_label`	`security_result.detection_fields`	Merged
`modifier_id_label`	`security_result.detection_fields`	Merged
`next_step_reservation_uid_label`	`security_result.detection_fields`	Merged
`object_appreciation_label`	`security_result.detection_fields`	Merged
`object_depreciation_label`	`security_result.detection_fields`	Merged
`oid_label`	`security_result.detection_fields`	Merged
`old_id_label`	`security_result.detection_fields`	Merged
`parent_action_object_id_label`	`security_result.detection_fields`	Merged
`predefined_forms_label`	`security_result.detection_fields`	Merged
`reservation_notification_label`	`security_result.detection_fields`	Merged
`reservation_uid_label`	`security_result.detection_fields`	Merged
`returned_label`	`security_result.detection_fields`	Merged
`state_label`	`security_result.detection_fields`	Merged
`status_label`	`security_result.detection_fields`	Merged
`updated_at_label`	`security_result.detection_fields`	Merged
`user_id_label`	`security_result.detection_fields`	Merged
`version_label`	`security_result.detection_fields`	Merged
N/A	`metadata.event_type`	Constant: `USER_UNCATEGORIZED`

Change Log

View the Change Log for this parser

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