1. Securing AI Endpoints by Operation with Azure API Management


    To secure AI endpoints with Azure API Management, you can create an API Management instance, define an API that references your AI service, and apply policies for securing the operations of that API. Azure API Management provides a layer of abstraction between the AI services and their consumers, offering features such as user authentication, rate limiting, and more.

    Below is a guide with a Pulumi program in Python detailing how to secure AI endpoints with Azure API Management. We will create an API Management instance, an API referring to an AI service (for demonstration, the service details are mocked), and a policy to require subscription key validation. Please adjust the service_url to the actual URL of your AI service.

    Step-by-Step Guide

    1. Service Creation: We will create an API Management service instance. This instance will become the gatekeeper for all calls to your AI services.
    2. API Definition: We will define an API that will act as a proxy to the real AI service. This involves setting up the service URL and the operations it exposes.
    3. Policy Application: We will apply policies to secure the API. Policies can enforce various constraints like quotas, key validation, IP filters, etc. For simplicity, we use a basic policy requiring a subscription key for API calls.

    Here's the Pulumi program that performs these steps:

    # A Pulumi program to secure AI endpoints with Azure API Management import pulumi import pulumi_azure_native as azure_native # Step 1: Create an API Management service instance api_management_service = azure_native.apimanagement.Service( "myApiManagementService", resource_group_name="<specify-your-resource-group-name>", # Replace with your resource group name # Other required properties like location, publisher email, publisher name, etc. location="West US", publisher_email="contact@mycompany.com", publisher_name="My Company", sku=azure_native.apimanagement.ServiceSkuDescriptionArgs( name=azure_native.apimanagement.SkuType.DEVELOPER, # Choose between Consumption, Basic, Standard, Premium, and Developer tiers capacity=1, ), ) # Step 2: Define an API in the API Management instance api = azure_native.apimanagement.Api( "myApi", resource_group_name=api_management_service.resource_group_name, service_name=api_management_service.name, display_name="AI Services API", path="ai-services", service_url="https://my-ai-service.azurewebsites.net", # Replace with the real URL of your AI service protocols=["https"], ) # Step 3: Apply a policy to the API requiring a subscription key global_policy = azure_native.apimanagement.Policy( "myGlobalPolicy", resource_group_name=api_management_service.resource_group_name, service_name=api_management_service.name, value=""" <policies> <inbound> <check-header name="Ocp-Apim-Subscription-Key" failed-check-httpcode="401" failed-check-error-message="Access denied due to missing subscription key. Make sure to include subscription key when making requests to an API."/> </inbound> <backend> <forward-request/> </backend> <outbound/> </policies> """, format="xml", ) # Export the API Management Service URL pulumi.export("api_management_service_url", api_management_service.gateway_url)

    Replace <specify-your-resource-group-name> with your Azure resource group name, and https://my-ai-service.azurewebsites.net with the URL of your actual AI service.

    This program is a starting point to securing your AI endpoints. As you become more familiar with Azure API Management, you can add more sophisticated policies for stronger security, like OAuth2 token validation, IP whitelisting, or transforming requests and responses.

    When you deploy this program with Pulumi, it will provision the resources described with the Azure cloud provider. Running the Pulumi up command will initiate the deployment process.

    For comprehensive policies and advanced security configurations, it is highly recommended to refer to the Azure API Management Policy documentation.