🚫 🔍 [DEPRECATED] Google Cloud Platform Cloud Monitoring

🚫 Deprecated: This connector has been deprecated and may be removed in future versions.

🔍 Discovered: This item was discovered by scanning the solution folder but is not listed in the Solution JSON file.

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Attribute Value
Connector ID GCPMonitorDataConnector
Publisher Google
Used in Solutions Google Cloud Platform Cloud Monitoring
Collection Method Azure Function
Connector Definition Files GCP_Monitor_API_FunctionApp.json
Ingestion API HTTP Data Collector API — Azure Function code uses SharedKey/HTTP Data Collector API
Custom Log V1 Tables Yes 🔶 — ingests into tables with type-suffixed columns

The Google Cloud Platform Cloud Monitoring data connector provides the capability to ingest GCP Monitoring metrics into Microsoft Sentinel using the GCP Monitoring API. Refer to GCP Monitoring API documentation for more information.

NOTE: This data connector has been deprecated, consider moving to the CCF data connector available in the solution which replaces ingestion via the deprecated HTTP Data Collector API.

Additional Information

📖 Setup Guide: Google Cloud Platform connectors - Connect GCP logs to Microsoft Sentinel

Tables Ingested

This connector ingests data into the following tables:

Table Transformations Ingestion API Lake-Only
GCP_MONITORING_CL 🔶 ? ✓ ?

💡 Tip: Tables with Ingestion API support allow data ingestion via the Azure Monitor Data Collector API, which also enables custom transformations during ingestion.

Permissions

Resource Provider Permissions: - Workspace (Workspace): read and write permissions on the workspace are required. - Keys (Workspace): read permissions to shared keys for the workspace are required. See the documentation to learn more about workspace keys.

Custom Permissions: - Microsoft.Web/sites permissions: Read and write permissions to Azure Functions to create a Function App is required. See the documentation to learn more about Azure Functions. - GCP service account: GCP service account with permissions to read Cloud Monitoring metrics is required for GCP Monitoring API (required Monitoring Viewer role). Also json file with service account key is required. See the documentation to learn more about creating service account and creating service account key.

Setup Instructions

⚠️ Note: These instructions were automatically generated from the connector's user interface definition file using AI and may not be fully accurate. Please verify all configuration steps in the Microsoft Sentinel portal.

NOTE: This connector uses Azure Functions to connect to the GCP API to pull logs into Microsoft Sentinel. This might result in additional data ingestion costs. Check the Azure Functions pricing page for details.

(Optional Step) Securely store workspace and API authorization key(s) or token(s) in Azure Key Vault. Azure Key Vault provides a secure mechanism to store and retrieve key values. Follow these instructions to use Azure Key Vault with an Azure Function App.

NOTE: This data connector depends on a parser based on a Kusto Function to work as expected GCP_MONITORING which is deployed with the Microsoft Sentinel Solution.

STEP 1 - Configuring GCP and obtaining credentials

  1. Create service account with Monitoring Viewer role and get service account key json file.

  2. Prepare the list of GCP projects to get metrics from. Learn more about GCP projects.

  3. Prepare the list of GCP metric types

STEP 2 - Choose ONE from the following two deployment options to deploy the connector and the associated Azure Function

IMPORTANT: Before deploying the data connector, have the Workspace ID and Workspace Primary Key (can be copied from the following), as well as Azure Blob Storage connection string and container name, readily available. - Workspace ID: WorkspaceId Note: The value above is dynamically provided when these instructions are presented within Microsoft Sentinel. - Primary Key: PrimaryKey Note: The value above is dynamically provided when these instructions are presented within Microsoft Sentinel.

3. Option 1 - Azure Resource Manager (ARM) Template

Use this method for automated deployment of the data connector using an ARM Template.

  1. Click the Deploy to Azure button below.

    Deploy To Azure 2. Select the preferred Subscription, Resource Group and Location. 3. Enter the Google Cloud Platform Project Id List, Google Cloud Platform Metric Types List, Google Cloud Platform Credentials File Content, Microsoft Sentinel Workspace Id, Microsoft Sentinel Shared Key 4. Mark the checkbox labeled I agree to the terms and conditions stated above. 5. Click Purchase to deploy.

4. Option 2 - Manual Deployment of Azure Functions

Use the following step-by-step instructions to deploy the data connector manually with Azure Functions (Deployment via Visual Studio Code).

1. Deploy a Function App

NOTE: You will need to prepare VS code for Azure function development.

  1. Download the Azure Function App file. Extract archive to your local development computer.
  2. Start VS Code. Choose File in the main menu and select Open Folder.
  3. Select the top level folder from extracted files.
  4. Choose the Azure icon in the Activity bar, then in the Azure: Functions area, choose the Deploy to function app button. If you aren't already signed in, choose the Azure icon in the Activity bar, then in the Azure: Functions area, choose Sign in to Azure If you're already signed in, go to the next step.

  5. Provide the following information at the prompts:

    a. Select folder: Choose a folder from your workspace or browse to one that contains your function app.

    b. Select Subscription: Choose the subscription to use.

    c. Select Create new Function App in Azure (Don't choose the Advanced option)

    d. Enter a globally unique name for the function app: Type a name that is valid in a URL path. The name you type is validated to make sure that it's unique in Azure Functions.

    e. Select a runtime: Choose Python 3.11.

    f. Select a location for new resources. For better performance and lower costs choose the same region where Microsoft Sentinel is located.

  6. Deployment will begin. A notification is displayed after your function app is created and the deployment package is applied.

  7. Go to Azure Portal for the Function App configuration.

2. Configure the Function App

  1. In the Function App, select the Function App Name and select Configuration.
  2. In the Application settings tab, select + New application setting.
  3. Add each of the following application settings individually, with their respective string values (case-sensitive): GCP_PROJECT_ID GCP_METRICS GCP_CREDENTIALS_FILE_CONTENT WORKSPACE_ID SHARED_KEY logAnalyticsUri (Optional) - Use logAnalyticsUri to override the log analytics API endpoint for dedicated cloud. For example, for public cloud, leave the value empty; for Azure GovUS cloud environment, specify the value in the following format: https://WORKSPACE_ID.ods.opinsights.azure.us.
  4. Once all application settings have been entered, click Save.

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