Collect TeamViewer logs

Supported in:

Google secops SIEM

This document explains how to ingest TeamViewer logs to Google Security Operations using Google Cloud Storage. The parser extracts the audit events from JSON formatted logs. It iterates through event details, mapping specific properties to Unified Data Model (UDM) fields, handling participant and presenter information, and categorizing events based on user activity. The parser also performs data transformations, such as merging labels and converting timestamps to a standardized format.

Before you begin

Ensure 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 TeamViewer Management Console
TeamViewer Business, Premium, Corporate, or Tensor license (required for API access)

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, `teamviewer-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.

Get TeamViewer prerequisites

Sign in to the TeamViewer Management Console at https://login.teamviewer.com/.
Click on your user icon in the upper right corner and select Edit profile.
Select Apps.
Click Create script token.
Provide the following configuration details:
- Token name: Enter a descriptive name (for example, Google SecOps Integration).
- Permissions: Select the following permissions:
  - Account management > View account data
  - Session management > View session data
  - Connection reporting > View connection reports
Click Create.
Copy and save the generated script token in a secure location.

Note: The script token will only be displayed once. If you lose it, you will need to create a new token. The script token must include the Connection reporting permission corresponding to the Connections.Read scope required by /api/v1/reports/connections.
Record your TeamViewer API Base URL: https://webapi.teamviewer.com/api/v1

Verify permissions

To verify the account has the required permissions:

Sign in to the TeamViewer Management Console.
Go to Edit profile > Apps.
Locate your script token in the list.
Verify that Connection reporting > View connection reports is enabled.
If this permission is not enabled, edit the token and add the required permission.

Test API access

Test your credentials before proceeding with the integration:

# Replace with your actual script token
SCRIPT_TOKEN="your-script-token"
API_BASE="https://webapi.teamviewer.com/api/v1"

# Test API access
curl -v -H "Authorization: Bearer ${SCRIPT_TOKEN}" \
  -H "Accept: application/json" \
  "${API_BASE}/reports/connections?from_date=2024-01-01T00:00:00Z&to_date=2024-01-01T01:00:00Z"

If you receive a 200 response with JSON data, your credentials are configured correctly.

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 teamviewer-collector-sa.
- Service account description: Enter Service account for Cloud Run function to collect TeamViewer 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, teamviewer-logs).
Go to the Permissions tab.
Click Grant access.
Provide the following configuration details:
- Add principals: Enter the service account email (for example, teamviewer-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 teamviewer-logs-trigger.
- Leave other settings as default.
Click Create.

Create Cloud Run function to collect logs

The Cloud Run function is triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from TeamViewer API and writes 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 teamviewer-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 Pub/Sub topic (teamviewer-logs-trigger).
4. Click Save.
In the Authentication section:
1. Select Require authentication.
2. Check Identity and Access Management (IAM).
Note: Pub/Sub automatically handles authentication when invoking the function.
Scroll down and expand Containers, Networking, Security.
Go to the Security tab:
- Service account: Select the service account (teamviewer-collector-sa).

Setting	Value
Service name	`teamviewer-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
`GCS_BUCKET`	`teamviewer-logs`
`GCS_PREFIX`	`teamviewer/audit/`
`STATE_KEY`	`teamviewer/audit/state.json`
`WINDOW_SECONDS`	`3600`
`HTTP_TIMEOUT`	`60`
`MAX_RETRIES`	`3`
`USER_AGENT`	`teamviewer-to-gcs/1.0`
`SCRIPT_TOKEN`	`your-script-token` (from TeamViewer prerequisites)
`API_BASE_URL`	`https://webapi.teamviewer.com/api/v1`

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 opens 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 urllib.request
import urllib.parse
import urllib.error
from datetime import datetime, timezone
import time
import uuid

# Initialize Storage client
storage_client = storage.Client()

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

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

    # Get environment variables
    bucket_name = os.environ.get('GCS_BUCKET')
    prefix = os.environ.get('GCS_PREFIX', 'teamviewer/audit/')
    state_key = os.environ.get('STATE_KEY', 'teamviewer/audit/state.json')
    window_sec = int(os.environ.get('WINDOW_SECONDS', '3600'))
    http_timeout = int(os.environ.get('HTTP_TIMEOUT', '60'))
    max_retries = int(os.environ.get('MAX_RETRIES', '3'))
    user_agent = os.environ.get('USER_AGENT', 'teamviewer-to-gcs/1.0')

    # TeamViewer API credentials
    api_base_url = os.environ.get('API_BASE_URL')
    script_token = os.environ.get('SCRIPT_TOKEN')

    if not all([bucket_name, api_base_url, script_token]):
        print('Error: Missing required environment variables')
        return

    try:
        # Get GCS bucket
        bucket = storage_client.bucket(bucket_name)

        # Load state (last processed timestamp)
        state = load_state(bucket, state_key)
        now = time.time()
        from_ts = float(state.get('last_to_ts') or (now - window_sec))
        to_ts = now

        print(f'Fetching TeamViewer audit data from {iso_format(from_ts)} to {iso_format(to_ts)}')

        # Build audit API URL
        url = build_audit_url(api_base_url, from_ts, to_ts)

        print(f'Fetching TeamViewer audit data from: {url}')

        # Fetch audit data with retries and pagination
        all_records = []
        offset_id = None

        while True:
            blob_data, content_type, next_offset = fetch_audit_data(
                url, script_token, user_agent, http_timeout, max_retries, offset_id
            )

            # Validate JSON data
            try:
                audit_data = json.loads(blob_data)
                records = audit_data.get('records', [])
                all_records.extend(records)
                print(f"Retrieved {len(records)} audit records (total: {len(all_records)})")

                # Check for pagination
                if next_offset and len(records) == 1000:
                    offset_id = next_offset
                    print(f"Fetching next page with offset_id: {offset_id}")
                else:
                    break

            except json.JSONDecodeError as e:
                print(f"Warning: Invalid JSON received: {e}")
                break

        if all_records:
            # Write to GCS
            key = put_audit_data(bucket, prefix, json.dumps({'records': all_records}), 
                               'application/json', from_ts, to_ts)
            print(f'Successfully wrote {len(all_records)} audit records to {key}')
        else:
            print('No audit records found')

        # Update state
        state['last_to_ts'] = to_ts
        state['last_successful_run'] = now
        save_state(bucket, state_key, state)

    except Exception as e:
        print(f'Error processing TeamViewer 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: {str(e)}')
    return {}

def save_state(bucket, key, state):
    """Save state to GCS."""
    try:
        blob = bucket.blob(key)
        blob.upload_from_string(
            json.dumps(state, separators=(',', ':')),
            content_type='application/json'
        )
    except Exception as e:
        print(f'Warning: Could not save state: {str(e)}')

def iso_format(ts):
    """Convert Unix timestamp to ISO 8601 format."""
    return time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime(ts))

def build_audit_url(api_base_url, from_ts, to_ts):
    """Build URL for TeamViewer audit API endpoint."""
    base_endpoint = f"{api_base_url.rstrip('/')}/reports/connections"
    params = {
        'from_date': iso_format(from_ts),
        'to_date': iso_format(to_ts)
    }
    query_string = urllib.parse.urlencode(params)
    return f"{base_endpoint}?{query_string}"

def fetch_audit_data(url, script_token, user_agent, http_timeout, max_retries, offset_id=None):
    """Fetch audit data from TeamViewer API with retries and pagination support."""
    # Add offset_id parameter if provided
    if offset_id:
        separator = '&' if '?' in url else '?'
        url = f"{url}{separator}offset_id={offset_id}"

    attempt = 0
    while True:
        req = urllib.request.Request(url, method='GET')
        req.add_header('User-Agent', user_agent)
        req.add_header('Authorization', f'Bearer {script_token}')
        req.add_header('Accept', 'application/json')

        try:
            with urllib.request.urlopen(req, timeout=http_timeout) as r:
                response_data = r.read()
                content_type = r.headers.get('Content-Type') or 'application/json'

                # Extract next_offset from response if present
                try:
                    data = json.loads(response_data)
                    next_offset = data.get('next_offset')
                except:
                    next_offset = None

                return response_data, content_type, next_offset

        except urllib.error.HTTPError as e:
            if e.code == 429:
                attempt += 1
                print(f'Rate limited (429) on attempt {attempt}')
                if attempt > max_retries:
                    raise
                time.sleep(min(60, 2 ** attempt) + (time.time() % 1))
            else:
                print(f'HTTP error {e.code}: {e.reason}')
                raise
        except urllib.error.URLError as e:
            attempt += 1
            print(f'URL error on attempt {attempt}: {e}')
            if attempt > max_retries:
                raise
            time.sleep(min(60, 2 ** attempt) + (time.time() % 1))

def put_audit_data(bucket, prefix, blob_data, content_type, from_ts, to_ts):
    """Write audit data to GCS."""
    ts_path = time.strftime('%Y/%m/%d', time.gmtime(to_ts))
    uniq = f"{int(time.time() * 1e6)}_{uuid.uuid4().hex[:8]}"
    key = f"{prefix}{ts_path}/teamviewer_audit_{int(from_ts)}_{int(to_ts)}_{uniq}.json"

    blob = bucket.blob(key)
    blob.metadata = {
        'source': 'teamviewer-audit',
        'from_timestamp': str(int(from_ts)),
        'to_timestamp': str(int(to_ts))
    }
    blob.upload_from_string(blob_data, content_type=content_type)

    return key

Second file: requirements.txt:

functions-framework==3.*
google-cloud-storage==2.*

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

Note: The /api/v1/reports/connections endpoint is limited to 1000 records per call. The implementation reads next_offset from the response and loops with offset_id until all records are retrieved.

Create Cloud Scheduler job

Cloud scheduler publishes 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	`teamviewer-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 Pub/Sub topic (`teamviewer-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 (teamviewer-logs-collector-hourly).
Click Force run to trigger the job manually.
Wait a few seconds.
Go to Cloud Run > Services.
Click on the function name (teamviewer-logs-collector).
Click the Logs tab.

Verify the function executed successfully. Look for the following:

Fetching TeamViewer audit data from YYYY-MM-DDTHH:MM:SSZ to YYYY-MM-DDTHH:MM:SSZ
Retrieved X audit records (total: X)
Successfully wrote X audit records to teamviewer/audit/YYYY/MM/DD/teamviewer_audit_...json

Go to Cloud Storage > Buckets.
Click your bucket name (teamviewer-logs).
Navigate to the prefix folder (teamviewer/audit/).
Verify that a new .json file was created with the current timestamp.

If you see errors in the logs:

HTTP 401: Check the SCRIPT_TOKEN environment variable matches your TeamViewer script token
HTTP 403: Verify the script token has Connection reporting > View connection reports permission
HTTP 429: Rate limiting - function will automatically retry with exponential backoff
Missing environment variables: Check all required variables (GCS_BUCKET, API_BASE_URL, SCRIPT_TOKEN) are set

Retrieve the Google SecOps service account

Google SecOps uses a unique service account to read data from your GCS bucket. You must grant this service account access to your bucket.

Get the service account email

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, TeamViewer logs).
Select Google Cloud Storage V2 as the Source type.
Select TeamViewer as the Log type.
Click Get Service Account. A unique service account email is 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.

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 (teamviewer-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.

Configure a feed in Google SecOps to ingest TeamViewer 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, TeamViewer logs).
Select Google Cloud Storage V2 as the Source type.
Select TeamViewer as the Log type.
Click Next.
Specify values for the following input parameters:
- Storage bucket URL: Enter the GCS bucket URI with the prefix path:
```
gs://teamviewer-logs/teamviewer/audit/
```
  - Replace:
    - teamviewer-logs: Your GCS bucket name.
    - teamviewer/audit/: Prefix/folder path where logs are stored.
  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.

UDM mapping table

Log field	UDM mapping	Logic
AffectedItem	metadata.product_log_id	The value of AffectedItem from the raw log is directly mapped to this UDM field.
EventDetails.NewValue	principal.resource.attribute.labels.value	If PropertyName contains (server), the NewValue is used as the value of a label in principal.resource.attribute.labels.
EventDetails.NewValue	principal.user.user_display_name	If PropertyName is Name of participant, the NewValue is used as the user display name for the principal.
EventDetails.NewValue	principal.user.userid	If PropertyName is ID of participant, the NewValue is used as the user ID for the principal.
EventDetails.NewValue	security_result.about.labels.value	For all other PropertyName values (except those handled by specific conditions), the NewValue is used as the value of a label within the security_result.about.labels array.
EventDetails.NewValue	target.file.full_path	If PropertyName is Source file, the NewValue is used as the full path for the target file.
EventDetails.NewValue	target.resource.attribute.labels.value	If PropertyName contains (client), the NewValue is used as the value of a label in target.resource.attribute.labels.
EventDetails.NewValue	target.user.user_display_name	If PropertyName is Name of presenter, the NewValue is parsed. If it's an integer, it's discarded. Otherwise, it's used as the user display name for the target.
EventDetails.NewValue	target.user.userid	If PropertyName is ID of presenter, the NewValue is used as the user ID for the target.
EventDetails.PropertyName	principal.resource.attribute.labels.key	If PropertyName contains (server), the PropertyName is used as the key of a label in principal.resource.attribute.labels.
EventDetails.PropertyName	security_result.about.labels.key	For all other PropertyName values (except those handled by specific conditions), the PropertyName is used as the key of a label within the security_result.about.labels array.
EventDetails.PropertyName	target.resource.attribute.labels.key	If PropertyName contains (client), the PropertyName is used as the key of a label in target.resource.attribute.labels.
EventName	metadata.product_event_type	The value of EventName from the raw log is directly mapped to this UDM field.
Timestamp	metadata.event_timestamp	The value of Timestamp from the raw log is parsed and used as the event timestamp in the metadata.
metadata.event_type		Set to USER_UNCATEGORIZED if src_user (derived from ID of participant) is not empty, otherwise set to USER_RESOURCE_ACCESS.
metadata.vendor_name		Hardcoded to TEAMVIEWER.
metadata.product_name		Hardcoded to TEAMVIEWER.
network.application_protocol		Hardcoded to TEAMVIEWER.

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