API Gateway Authorizer for Controlled Access to AI APIs

Question

Pulumi · Accepted Answer

To create an API Gateway with an Authorizer for controlled access to your AI APIs, we will implement the following steps using Pulumi with AWS:

1. **Set up an API Gateway**: This acts as the entry point for your AI APIs.
2. **Create an Authorizer**: This defines the authorization mechanism. We can use a Lambda function here to validate tokens, or AWS IAM policies depending on the use case.
3. **Define Resources and Methods**: Resources are your API endpoints, and methods are the HTTP verbs (GET, POST, PUT, etc.) that your API supports.
4. **Secure Methods with the Authorizer**: Attach your Authorizer to methods to protect them.

Below you will find a Pulumi program written in Python that provisions an AWS API Gateway with a Lambda Authorizer for securing access. We'll use the `aws.apigateway.Authorizer` resource to create the Authorizer.

Before diving into the code, ensure that you've set up your Pulumi and AWS CLI with the appropriate credentials and configurations.

Now, let's write the Pulumi program:

```python
import json
import pulumi
import pulumi_aws as aws

# Create a new Lambda function to use as the Authorizer.
# This function will validate the token sent with the request
# and must return an IAM policy.
authorizer_lambda = aws.lambda_.Function('apiAuthorizerLambda',
    runtime='python3.8',
    role=lambda_role.arn,
    handler='index.handler',
    code=pulumi.FileArchive('./authorizer_lambda'))

# Define the API Gateway
api = aws.apigateway.RestApi('myApi',
    description='API for AI Services')

# Create an Authorizer for the API Gateway that references the Lambda function.
authorizer = aws.apigateway.Authorizer('myApiAuthorizer',
    rest_api=api.id,
    authorizer_uri=authorizer_lambda.invoke_arn,
    type='TOKEN',
    identity_source='method.request.header.Authorization')

# Create a resource representing the AI service endpoint.
ai_resource = aws.apigateway.Resource('aiResource',
    rest_api=api.id,
    parent_id=api.root_resource_id,
    path_part='ai-service')

# Define the HTTP method for the AI service endpoint and secure it with the Authorizer.
ai_method = aws.apigateway.Method('aiMethod',
    rest_api=api.id,
    resource_id=ai_resource.id,
    http_method='GET',  # Change to the HTTP method you require (GET, POST, etc.)
    authorization='CUSTOM',
    authorizer_id=authorizer.id)

# Deploy the API Gateway.
deployment = aws.apigateway.Deployment('apiDeployment',
    rest_api=api.id,
    # Pulumi will auto-name the stage, but you can set a specific name if preferred.
    stage_name='v1',
    # Ensure that changes to the API or its methods trigger a new deployment.
    triggers={
        'deployment': pulumi.Output.all(
            ai_method.http_method,  # Add any other methods you define.
        ).apply(lambda x: json.dumps(x))
    },
    opts=pulumi.ResourceOptions(depends_on=[ai_method]))

# Output the URL of the AI service endpoint
pulumi.export('ai_service_url', deployment.invoke_url.apply(lambda url: f'{url}ai-service'))
```

In this program, `authorizer_lambda` defines a new AWS Lambda function that is used as the authorizer. This Lambda function is expected to take a provided token, validate it, and return an appropriate IAM policy. The actual token validation logic should be implemented in the code of this Lambda function.

The `api` object is an instance of `aws.apigateway.RestApi` that defines the container for your different API resources and methods.

The `authorizer` object is an instance of `aws.apigateway.Authorizer` and connects our Lambda function to the API Gateway as the authorizer.

The `ai_resource` and `ai_method` objects represent the path and method of our AI service respectively. The `ai_method` is secured using the CUSTOM authorization type, which means it will utilize the provided authorizer.

Finally, the `deployment` object deploys the API and the `pulumi.export` statement makes the API endpoint accessible as a stack output.

Remember to replace `'index.handler'` with the correct handler location for your Lambda function, and to upload the Lambda function code in `'./authorizer_lambda'`. Also, replace `'method.request.header.Authorization'` with the appropriate header through which your client will pass the access token.