Implement Azure AI Content Safety

In this exercise, you will provision a Content Safety resource, test the resource in Azure AI Foundry, and test the resource in code.

Provision a Content Safety resource

If you don’t already have one, you’ll need to provision a Content Safety resource in your Azure subscription.

Open the Azure portal at https://portal.azure.com, and sign in using the Microsoft account associated with your Azure subscription.
Select Create a resource.
In the search field, search for Content Safety. Then, in the results, select Create under Azure AI Content Safety.
Provision the resource using the following settings:
- Subscription: Your Azure subscription.
- Resource group: Choose or create a resource group.
- Region: Select East US
- Name: Enter a unique name.
- Pricing tier: Select F0 (free), or S (standard) if F0 is not available.
Select Review + create, then select Create to provision the resource.
Wait for deployment to complete, and then go to the resource.
Select Access Control in the left navigation bar, then select + Add and Add role assignment.
Scroll down to choose the Cognitive Services User role and select Next.
Add your account to this role, and then select Review + assign.
Select Resource Management in the left hand navigation bar and select Keys and Endpoint. Leave this page open so you can copy the keys later.

Use Azure AI Content Safety Prompt Shields

In this exercise you will use Azure AI Foundry to test Content Safety Prompt Shields with two sample inputs. One simulates a user prompt, and the other simulates a document with potentially unsafe text embedded into it.

In another browser tab, open the Content Safety page of Azure AI Foundry and sign in.
Under Moderate text content select Try it out.
On the Moderate text content page, under Azure AI Services select the Content Safety resource you created earlier.
Select Multiple risk categories in one sentence. Review the document text for potential issues.
Select Run test and review the results.
Optionally, alter the threshold levels and select Run test again.
On the Moderate text content drop-down menu at the top of the page, select Protected material detection for text.
Select Protected lyrics and note that these are the lyrics of a published song.
Select Run test and review the results.
On the Protected material detection for text drop-down menu at the top of the page, select Moderate image content.
Select Self-harm content.
Notice that all images are blurred by default in AI Foundry. You should also be aware that the sexual content in the samples is very mild.
Select Run test and review the results.
On the Moderate image content drop-down menu at the top of the page, select Prompt shields.
On the Prompt shields page, under Azure AI Services select the Content Safety resource you created earlier.
Select Prompt & document attack content. Review the user prompt and document text for potential issues.
Select Run test.
In View results, verify that Jailbreak attacks were detected in both the user prompt and the document.

[!TIP] Code is available for all of the samples in AI Foundry.
Under Next steps, under View the code select View code. The Sample code window is displayed.
Use the down arrow to select either Python or C# and then select Copy to copy the sample code to the clipboard.
Close the Sample code screen.

Clone the repository in Cloud Shell

You’ll develop your code using Cloud Shell from the Azure Portal. The code files for your app have been provided in a GitHub repo.

Tip: If you have already cloned the mslearn-ai-services repo recently, you can skip this task. Otherwise, follow these steps to clone it to your development environment.

In the Azure Portal, use the [>_] button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal.

Note: If you have previously created a cloud shell that uses a Bash environment, switch it to PowerShell.
In the cloud shell toolbar, in the Settings menu, select Go to Classic version (this is required to use the code editor).

Tip: As you paste commands into the cloudshell, the ouput may take up a large amount of the screen buffer. You can clear the screen by entering the cls command to make it easier to focus on each task.

In the PowerShell pane, enter the following commands to clone the GitHub repo for this exercise:

 rm -r mslearn-ai-services -f
 git clone https://github.com/microsoftlearning/mslearn-ai-services mslearn-ai-services

After the repo has been cloned, navigate to the language-specific folder containing the application code files, based on the programming language of your choice (Python or C#):

Python
```
cd mslearn-ai-services/Labfiles/05-implement-content-safety/Python
```
C#
```
cd mslearn-ai-services/Labfiles/05-implement-content-safety/C-Sharp
```
If you chose Python, enter the following command to install the library you’ll use:

Python
```
python -m venv labenv
./labenv/bin/Activate.ps1
pip install requests
```
Next, enter the following command to edit the application file that has been provided:

Python
```
code prompt-shield-py
```
C#
```
code Program.cs
```
The file is opened in a code editor.
Scroll down to find Replace with your own subscription _key and endpoint.
In the Azure portal, on the Keys and Endpoint page, copy one of the Keys (1 or 2). Replace **** with this value.
In the Azure portal, on the Keys and Endpoint page, copy the Endpoint. Paste this value into your code to replace ****.
In Azure AI Foundry, copy the User prompt value. Paste this into your code to replace **** (Python) or **** (C#).
Scroll down to **** and delete this line of code.
In Azure AI Foundry, copy the Document value.
Scroll down to **** and paste your document value.
After you’ve replaced the placeholders, use the CTRL+S command to save your changes and then use the CTRL+Q command to close the code editor while keeping the cloud shell command line open.
Run the program and verify that an attack was detected:

Python
```
python prompt-shield-py
```
C#
```
dotnet run
```
Optionally, you can experiment with different test content and document values.

Clean up resources

If you’re not using the Azure resources created in this lab for other training modules, you can delete them to avoid incurring further charges.

Open the Azure portal at https://portal.azure.com, and in the top search bar, search for the resources you created in this lab.
On the resource page, select Delete and follow the instructions to delete the resource. Alternatively, you can delete the entire resource group to clean up all resources at the same time.