> [!WARNING]
> **Developer Preview:** Available as part of the [Google Workspace Developer Preview Program](https://developers.google.com/workspace/preview.md.txt), which grants early access to certain features.

This page describes how to configure security settings for Google Workspace
MCP servers.

You must screen prompts and responses for malicious content or prompt injection
attacks. You can use the Google-provided Model Armor, or you can use
your own solution if you document it in order for users to accept that risk.
For more information, see
[Google Workspace user data and developer policy](https://developers.google.com/workspace/workspace-api-user-data-developer-policy.md.txt).

## Prerequisites

- Membership in the
  [Google Workspace Developer Preview Program](https://developers.google.com/workspace/preview.md.txt).

- A Google Cloud project. To create a project, see
  [Create a project](https://developers.google.com/workspace/guides/create-project.md.txt).

- An OAuth 2.0 client ID with **Use this client for an AI-powered agent**
  selected in **Google Auth Platform** **\>** **Clients** . To create an OAuth
  client ID, see
  [Create OAuth client ID credentials](https://developers.google.com/workspace/guides/create-credentials.md.txt#oauth-client-id).

## Optional security and safety configurations

MCP introduces new security risks and considerations due to the wide variety of
actions that you can do with the MCP tools. To minimize and manage these risks,
Google Cloud offers default settings and customizable
policies to control the use of MCP tools in your Google Cloud
organization or project.

For more information about MCP security and governance, see
[AI security and safety](https://docs.cloud.google.com/mcp/ai-security-safety).

### Use Model Armor

[Model Armor](https://docs.cloud.google.com/model-armor/overview) is a
Google Cloud service designed to enhance the security and
safety of your AI applications. It works by proactively screening LLM prompts
and responses, protecting against various risks and supporting responsible AI
practices. Whether you are deploying AI in your cloud environment, or on
external cloud providers, Model Armor can help
you prevent malicious input, verify content safety, protect sensitive data,
maintain compliance, and enforce your AI safety and security policies
consistently across your diverse AI landscape.

When Model Armor is enabled with
[logging enabled](https://docs.cloud.google.com/model-armor/configure-logging), Model Armor logs the entire
payload. This might expose sensitive information in your logs.

#### MCP request routing to Model Armor

Model Armor is available in [certain regions](https://docs.cloud.google.com/model-armor/locations). When Model Armor is enabled and you use an MCP server in a jurisdiction that Model Armor doesn't support, the routing behavior of the call might be different for different MCP servers and might break data residency compliance for in-use and in-transit data. For more information about the behavior of individual MCP servers, see [Model Armor supported products](https://docs.cloud.google.com/mcp/model-armor-supported-products).

#### Enable Model Armor

You must enable Model Armor APIs before you can use Model Armor.

### Console

1.


   Enable the Model Armor API, if it is not already enabled.


   **Roles required to enable APIs**


   To enable APIs, you need the `serviceusage.services.enable` permission. If you
   created the project, then you likely already have this permission through the
   Owner role (`roles/owner`). Otherwise, you can get this permission through the
   Service Usage Admin role (`roles/serviceusage.serviceUsageAdmin`).
   [Learn how to grant roles](https://developers.google.com/iam/docs/granting-changing-revoking-access).

   [Enable the API](https://console.cloud.google.com/apis/enableflow?apiid=modelarmor.googleapis.com)

   <br />

2. Select the project where you want to activate Model Armor.

### gcloud

Before you begin, follow these steps using the Google Cloud CLI with the
Model Armor API:

1.


   [Install](https://developers.google.com/sdk/docs/install) the Google Cloud CLI, and then
   [sign in to the gcloud with your federated identity](https://developers.google.com/iam/docs/workforce-log-in-gcloud).

   After signing in,
   [initialize](https://developers.google.com/sdk/docs/initializing) the Google Cloud CLI by running the following command:

   ```bash
   gcloud init
   ```

   <br />

2.

   Run the following command to set the API endpoint for the
   Model Armor service.

   ```bash
   gcloud config set api_endpoint_overrides/modelarmor "https://modelarmor.LOCATION.rep.googleapis.com/"
   ```

   Replace `LOCATION` with the region where you want to use
   Model Armor.

   <br />

#### Configure protection for Google and Google Cloud remote MCP servers

To help protect your MCP tool calls and responses you can use
Model Armor floor settings. A floor setting defines the minimum
security filters that apply across the project. This configuration applies a
consistent set of filters to all MCP tool calls and responses within
the project.

> [!TIP]
> **Tip:** Don't enable the prompt injection and jailbreak filter unless your MCP traffic carries natural language data.

Set up a Model Armor floor setting with MCP sanitization
enabled. For more information, see [Configure Model Armor floor
settings](https://docs.cloud.google.com/model-armor/configure-floor-settings).

> [!NOTE]
> **Note:** If the agent and the MCP server are in different projects, you can create floor settings in both projects (the client project and the resource project). In this case, Model Armor is invoked twice, once for each project.

See the following example command:

```bash
gcloud model-armor floorsettings update \
--full-uri='projects/PROJECT_ID/locations/global/floorSetting' \
--enable-floor-setting-enforcement=TRUE \
--add-integrated-services=GOOGLE_MCP_SERVER \
--google-mcp-server-enforcement-type=INSPECT_AND_BLOCK \
--enable-google-mcp-server-cloud-logging \
--malicious-uri-filter-settings-enforcement=ENABLED \
--add-rai-settings-filters='[{"confidenceLevel": "MEDIUM_AND_ABOVE", "filterType": "DANGEROUS"}]'
```

Replace `PROJECT_ID` with your Google Cloud project ID.

Note the following settings:

- <var translate="no">`INSPECT_AND_BLOCK`</var>: The enforcement type that inspects content for the Google MCP server and blocks prompts and responses that match the filters.
- <var translate="no">`ENABLED`</var>: The setting that enables a filter or enforcement.
- <var translate="no">`MEDIUM_AND_ABOVE`</var>: The confidence level for the Responsible AI - Dangerous filter settings. You can modify this setting, though lower values might result in more false positives. For more information, see [Model Armor confidence levels](https://docs.cloud.google.com/model-armor/overview#ma-confidence-levels).

#### Disable scanning MCP traffic with Model Armor

To stop Model Armor from automatically scanning traffic to and
from Google MCP servers based on the project's floor settings, run the following
command:

```bash
gcloud model-armor floorsettings update \
  --full-uri='projects/PROJECT_ID/locations/global/floorSetting' \
  --remove-integrated-services=GOOGLE_MCP_SERVER
```

Replace `PROJECT_ID` with the Google Cloud project
ID. Model Armor doesn't automatically apply the rules defined in
this project's floor settings to any Google MCP server traffic.

Model Armor floor settings and general configuration can impact
more than just MCP. Because Model Armor integrates with services
like Vertex AI, any changes you make to floor settings can affect
traffic scanning and safety behaviors across all integrated services, not just
MCP.

## Related topics

- [Google Workspace user data and developer policy](https://developers.google.com/workspace/workspace-api-user-data-developer-policy.md.txt)
- [Model Armor overview](https://docs.cloud.google.com/model-armor/overview)
- [Google Workspace MCP servers in Antigravity (codelab)](https://codelabs.developers.google.com/google-workspace-mcp-antigravity#0)
- [List of Google Workspace MCP servers](https://developers.google.com/workspace/guides/configure-mcp-servers.md.txt#support-products)