1. IP-based Access Control for AI APIs

    Python

    IP-based Access Control is a security measure that restricts access to a service or an API by allowing only requests from specified IP addresses. To implement IP-based Access Control for AI APIs in a cloud environment, different cloud providers offer different mechanisms. I will illustrate how to use AWS to create an Amazon API Gateway with IP-based access control through a resource policy that only allows invocations from specified IP addresses.

    Here's what we'll do:

    1. Define an Amazon API Gateway REST API resource.
    2. Attach a resource policy to the API Gateway that specifies the allowed IP addresses or IP ranges.
    3. Deploy this API Gateway to make it accessible.

    I will guide you through the code step by step, explaining each part and why it's necessary.

    Pulumi Program in Python

    import pulumi import pulumi_aws as aws # Create an API Gateway REST API api_gateway = aws.apigateway.RestApi("api", description="API for AI services with IP-based Access Control") # Define the resource policy # Replace the IP placeholders with the actual IP addresses or ranges in CIDR notation resource_policy = { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": "*", "Action": "execute-api:Invoke", "Resource": "execute-api:/*/*/*", # Specify IP ranges here "Condition": { "IpAddress": { "aws:SourceIp": ["203.0.113.0/24", "198.51.100.0/24"] } } }] } # Attach the resource policy to the API Gateway api_gateway_policy = aws.apigateway.RestApiPolicy("apiPolicy", rest_api_id=api_gateway.id, policy=pulumi.Output.from_input(resource_policy).apply(lambda x: pulumi.json) ) # Deploy the API. This makes the API accessible and live. deployment = aws.apigateway.Deployment("apiDeployment", rest_api_id=api_gateway.id, # Setting stage name to 'prod'. Feel free to change it as needed. stage_name="prod") # Make sure to apply the policy before deploying the API deployment.depend_on(api_gateway_policy) # Export the invoke URL of the deployed API pulumi.export("invoke_url", deployment.invoke_url)

    Now let's go through the code together:

    • We created an API Gateway REST API with the name 'api.' This serves as an entry point for our AI services.
    • We defined a resource_policy that includes a statement allowing access only from certain IP addresses.
    • We set the Effect to "Allow," which means the policy permits actions.
    • The Principal is set to "*", which indicates the policy applies to all principals (i.e., any client).
    • The Action is "execute-api:Invoke," this means the policy allows invoking the API Gateway methods.
    • The Resource is set to "execute-api:///*," which means the policy applies to all methods of all APIs in this Gateway.
    • The Condition block specifies the source IP addresses or ranges permitted to access the API. Be sure to replace the "203.0.113.0/24", "198.51.100.0/24" with the IP ranges you want to allow.
    • We attach this policy to our API Gateway with aws.apigateway.RestApiPolicy. This resource connects our resource_policy to the API Gateway we defined earlier using its ID.
    • We create a deployment to make the API live and accessible using aws.apigateway.Deployment, which references our API Gateway's ID.
    • We ensure that the deployment depends on the policy attachment, which means the API does not go live before the policy is in effect.
    • Finally, we export the URL that you can use to invoke the API.

    This configuration sets up an Amazon API Gateway with an IP-based access control policy. It ensures only requests from the specified IP addresses can access the AI API endpoint, adding a layer of security to your setup.


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