GSP1327

Overview

Model Armor on Google Cloud Platform enhances AI safety by screening both user prompts and LLM responses for risks like harmful content, prompt injection, and sensitive data leaks. It allows organizations to centrally manage and enforce security policies for their AI applications, acting as a filter to ensure interactions are safe and compliant. This service integrates with Security Command Center for unified visibility and helps protect against unauthorized exposure or manipulation of AI models.

In this lab, you explore sanitizing prompts and their responses using Model Armor.

Objectives

This lab challenges you to demonstrate your ability to do the following:

• Enable the Model Armor API.
• Create a Model Armor template.
• Execute various commands for sanitizing user prompts against different security features.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Enable the Model Armor API

Before you can begin using Model Armor, you must enable the API.

1. In the Google Cloud console, activate Cloud Shell.

2. Execute the following command:

PROJECT=$(gcloud config get-value project) gcloud services enable modelarmor.googleapis.com --project=$PROJECT

Click Check my progress to verify the objective. Enable the Model Armor API

Task 2. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:

1. Close the browser tab for JupyterLab, and return to the Workbench home page.

2. Select the checkbox next to the instance name, and click Reset.

3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.

Task 3. Set up the notebook

1. Open the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. Run through the Getting Started and the Import libraries sections of the notebook.

   • For Project ID, use , and for Location, use .

Note: You can skip any notebook cells that are noted Colab only. If you experience a 429 response from any of the notebook cell executions, wait 1 minute before running the cell again to proceed.

Task 4. Create a Model Armor template

In this task, you create a Model Armor template ma-template to define security and content filtering rules for AI interactions. This template establishes policies for detecting and mitigating sensitive data exposure, ensuring compliance with data protection standards.

Run through the Create a Model Armor Template section of the notebook to create the ma-template in the location using the provided filter configuration. Once completed, return here to check your progress and verify the objective.

Note: Before clicking the Check my progress button, make sure you have saved the Notebook file.

Click Check my progress to verify the objective. Create a Model Armor template

Task 5. Validate user prompts with Model Armor

In this task, you test various user prompts to evaluate how Model Armor detects and screens different types of sensitive data based on predefined Sensitive Data Protection (SDP) infoTypes. This process helps ensure that Model Armor effectively filters and protects against unauthorized data exposure.

Go through the Validating User Prompts with Model Armor section of the notebook to execute various commands for sanitizing user prompts against different security features. Once complete, return here to check your progress and verify the objectives.

Note: Before clicking the Check my progress button, make sure you have saved the Notebook file.

1. Input a user prompt for Model Armor to screen Responsible AI.

Click Check my progress to verify the objective. Input a user prompt to screen Responsible AI

2. Input a user prompt for Model Armor to screen Malicious URI.

Click Check my progress to verify the objective. Input a user prompt to screen Malicious URI

3. Input a user prompt for Model Armor to screen DLP.

Click Check my progress to verify the objective. Input a user prompt to screen DLP

4. Input a model response for Model Armor to screen DLP.

Click Check my progress to verify the objective. Input a model response to screen DLP

5. Execute a file-based prompt to use Model Armor to sanitize a user prompt in the specified file format.

Click Check my progress to verify the objective. Execute a file-based prompt to sanitize a file format

Congratulations!

You have successfully completed the challenge to create a Model Armor template and you have executed different user prompts to screen for varying Sensitive Data Protection (SDP) infoTypes.

Manual Last Updated June 25, 2025

Lab Last Tested June 25, 2025

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