Quickstart: Protected material detection for code (preview)

The Protected Material for Code feature provides a comprehensive solution for identifying AI outputs that match code from existing GitHub repositories. This feature allows code generation models to be used confidently, in a way that enhances transparency to end users and promotes compliance with organizational policies.

The key objectives of the Protected Material Detection for Code feature for AI-generated code are:

• To detect and prevent the display of protected code generated by AI models.
• To enable organizations to manage risks associated with AI-generated code.
• To ensure that AI-generated code complies with legal, ethical, and organizational policy standards.

For more information about protected material detection, see the Protected material detection concept page. For API input limits, see the Input requirements section of the Overview.

Prerequisites

• An Azure subscription - Create one for free
• Once you have your Azure subscription, create a Content Safety resource in the Azure portal to get your key and endpoint. Enter a unique name for your resource, select your subscription, and select a resource group, supported region (see Region availability), and supported pricing tier. Then select Create.
  • The resource takes a few minutes to deploy. After it finishes, Select go to resource. In the left pane, under Resource Management, select Subscription Key and Endpoint. The endpoint and either of the keys are used to call APIs.
• cURL installed

Analyze code for protected material detection

The following section walks through a sample request with cURL. Paste the command below into a text editor, and make the following changes.

1. Replace <endpoint> with the endpoint URL associated with your resource.
2. Replace <your_subscription_key> with one of the keys that come with your resource.
3. Optionally, replace the "code" field in the body with your own Code you'd like to analyze.

   Tip

   See Input requirements for maximum Code length limitations. Protected material detection is meant to be run on LLM completions, not user prompts.

curl --location --request POST '<endpoint>/contentsafety/text:detectProtectedMaterialForCode?api-version=2024-09-15-preview' \
--header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
--header 'Content-Type: application/json' \
--data-raw '{
  "code": "python import pygame pygame.init() win = pygame.display.set_mode((500, 500)) pygame.display.set_caption(My Game) x = 50 y = 50 width = 40 height = 60 vel = 5 run = True while run: pygame.time.delay(100) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False keys = pygame.key.get_pressed() if keys[pygame.K_LEFT] and x > vel: x -= vel if keys[pygame.K_RIGHT] and x < 500 - width - vel: x += vel if keys[pygame.K_UP] and y > vel: y -= vel if keys[pygame.K_DOWN] and y < 500 - height - vel: y += vel win.fill((0, 0, 0)) pygame.draw.rect(win, (255, 0, 0), (x, y, width, height)) pygame.display.update() pygame.quit()"
}'

The below fields must be included in the url:

The parameters in the request body are defined in this table:

See the following sample value for the "code" field:

{
    "code": "python import pygame pygame.init() win = pygame.display.set_mode((500, 500)) pygame.display.set_caption(My Game) x = 50 y = 50 width = 40 height = 60 vel = 5 run = True while run: pygame.time.delay(100) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False keys = pygame.key.get_pressed() if keys[pygame.K_LEFT] and x > vel: x -= vel if keys[pygame.K_RIGHT] and x < 500 - width - vel: x += vel if keys[pygame.K_UP] and y > vel: y -= vel if keys[pygame.K_DOWN] and y < 500 - height - vel: y += vel win.fill((0, 0, 0)) pygame.draw.rect(win, (255, 0, 0), (x, y, width, height)) pygame.display.update() pygame.quit()"
}

Open a command prompt window and run the cURL command.

Interpret the API response

You should see the protected material detection results displayed as JSON data in the console output. For example:

{
    "protectedMaterialAnalysis": {
        "detected": true,
        "codeCitations": [
            {
                "license": "NOASSERTION",
                "sourceUrls": [
                    "https://github.com/kolejny-projekt-z-kck/game-/tree/f134099ce970da951bac9baac83c7885e991c676/ganeee.py",
                    "https://github.com/Felipe-Velasco/Modulo-Pygame/tree/11490c44a951812dc0c6424b68b1e14fc5cc4c0b/pygame%20basics.py",
                    "https://github.com/bwootton/firstgame/tree/70d722a6b1ccb79bfa56d9cc69932051848c44bf/jump.py",
                    "https://github.com/Jason017/Pygame-Learning-Module/tree/17cd69f169d3759e00816ed4a3795dd6db7e157f/pygameModule02.py",
                    "https://github.com/Coders-Brothers/pygame-tutorial/tree/1b481f5687cdda7c0765089780ef451af6e175cd/lesson-2.py"
                ]
            }
        ]
    }
}

The JSON fields in the output are defined here:

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

• Azure portal
• Azure CLI

Related content

• Protected material detection concepts
• Configure filters for each category and test on datasets using Content Safety Studio, export the code and deploy.