API Key-based Authorization for AI APIs

Question

Pulumi · Accepted Answer

When dealing with AI APIs, or any APIs in general, securing them is crucial. One common method to secure an API is to require an API Key with each request. An API key is a token that a client provides when making API calls. It's used to identify the client to the server, which can improve security by limiting access to trusted clients.

Let me walk you through setting up API Key-based authorization for an AI API using AWS services with Pulumi in Python.

1. **Amazon API Gateway**: We'll use this to create and manage our AI API.
2. **API Key**: We'll create an API key that clients must use to access our API.
3. **Usage Plan**: We'll associate our API key with a usage plan to throttle and manage usage.

Here's what the Pulumi program will look like:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Create an API Gateway Rest API
# This acts as a container for the resources that make up your AI API.
rest_api = aws.apigateway.RestApi("my-api",
    description="This is my API for AI services")

# Step 2: Create a resource
# A resource is a logical entity within your API. Here's an example resource.
resource = aws.apigateway.Resource("my-resource",
    rest_api_id=rest_api.id,
    parent_id=rest_api.root_resource_id,
    path_part="myresource")

# Step 3: Create a method for the resource
# The method defines how API Gateway responds to requests for the resource.
method = aws.apigateway.Method("my-method",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method="GET",
    authorization="NONE")

# Step 4: Create an API Key
# Clients will use this key to make requests to your API.
api_key = aws.apigateway.ApiKey("my-api-key",
    description="API Key for my AI API")

# Step 5: Create a Usage Plan
# Associate the API Key with specific throttle and quota limits.
usage_plan = aws.apigateway.UsagePlan("my-usage-plan",
    description="Usage plan for my AI API",
    api_stages=[aws.apigateway.UsagePlanApiStageArgs(
        api_id=rest_api.id,
        stage=resource.id,
    )],
    throttle=aws.apigateway.UsagePlanThrottleSettingsArgs(
        burst_limit=5,
        rate_limit=10
    ),
    quota=aws.apigateway.UsagePlanQuotaSettingsArgs(
        limit=1000,
        period="MONTH",
        offset=2
    ))

# Step 6: Link the Usage Plan with the API Key
key_usage = aws.apigateway.UsagePlanKey("my-key-usage",
    key_id=api_key.id,
    key_type="API_KEY",
    usage_plan_id=usage_plan.id)

# Output the API Key value and the API endpoint for client use
pulumi.export("api_key", api_key.value)
pulumi.export("api_endpoint", pulumi.Output.concat("https://", rest_api.id, ".execute-api.", aws.get_region().name, ".amazonaws.com/"))
```

In this program, we start by creating an API Gateway Rest API that represents our AI API. We then create a resource within this API, which is a logical entity that clients will be able to access—similar to an endpoint.

We move on to create a method for the resource, which defines how the API responds to client requests. For simplicity, I have chosen `authorization="NONE"` here, but you can opt for different authorization types depending on your use case.

Next, we generate an API Key. This key will need to be included in the header of requests made to the API to access AI services.

Then, we establish a Usage Plan which defines the quota and rate limits for the API key holder. The Usage Plan also associates our API stages with the plan, which is a way to manage deployments of your API.

Finally, we link the API Key with the Usage Plan and output the key value and API endpoint. The API Key is what clients will use to interact with the API securely.

The `pulumi.export` statements output the API Key and the endpoint URL after the deployment. Ensure that you keep your API keys secure and do not expose them in your client applications. Usually, API keys are added to HTTP requests in the header.

Remember that the above program defines infrastructure as code using Pulumi, which means upon execution, it will make changes to your AWS account, creating the defined resources. Therefore, ensure you have the necessary permissions and are aware of potential costs associated with the resources created.