Secure AI APIs with GCP API Gateway.

Question

Pulumi · Accepted Answer

Securing AI APIs on GCP (Google Cloud Platform) involves several steps, including creating an API Gateway, configuring an API with security policies, and applying IAM (Identity and Access Management) policies to control access.

Below is a Pulumi Python program that demonstrates how to:

1. Create an API on GCP API Gateway using the `gcp.apigateway.Api` resource.
2. Create an API config for the API using the `gcp.apigateway.ApiConfig` resource.
3. Bind IAM policies to the API config to secure it using the `gcp.apigateway.ApiConfigIamPolicy` resource.

For this example, we assume that you have an OpenAPI specification ready for your AI API, and it's available at a specific URL or a path in Cloud Storage. Consequently, this program doesn't include the specifics of setting up AI services, as it focuses on the API Gateway and IAM security aspects.

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables with appropriate values.
project = "your-gcp-project-id"
api_id = "your-api-id"
api_gateway_id = "your-gateway-id"
api_config_id = "your-api-config-id"
openapi_spec_path = "path/to/your/openapi/spec.yaml"  # This should be the path to your OpenAPI specification in Cloud Storage or other accessible URL.

# Create an API on GCP API Gateway.
ai_api = gcp.apigateway.Api("ai-api",
    api_id=api_id,
    project=project,
    labels={
        "purpose": "AI-API",
    },
    display_name="AI API")

# Create an API config for the API.
api_config = gcp.apigateway.ApiConfig("api-config",
    api_config_id=api_config_id,
    project=project,
    api=ai_api.api_id,
    openapi_documents=[{
        "document": {
            "path": openapi_spec_path,
            "contents": openapi_spec_contents,
        },
    }],
    display_name="AI API Config")

# Bind IAM policies to the API config to secure it.
# This example grants the 'roles/apigateway.invoker' role to a specific member.
api_config_iam_policy = gcp.apigateway.ApiConfigIamPolicy("api-config-iam-policy",
    project=project,
    api=ai_api.api_id,
    api_config=api_config.id,
    policy_data=pulumi.Output.all(ai_api.api_id, api_config.id).apply(
        lambda args: json.dumps({
            "bindings": [{
                "role": "roles/apigateway.invoker",
                "members": ["user:someone@example.com"],
            }],
        })
    ))

# Export the URL of the deployed API Gateway.
pulumi.export('gateway_url', pulumi.Output.concat('https://', api_gateway_id, '-', project, '.apigateway.cloud.google.com'))

```
In this program:

- We create an `Api` resource named `ai-api`, which corresponds to an abstraction of our AI services. We give it a unique `api_id` and label it with a purpose.

- We create an `ApiConfig` resource named `api-config`. An API Config in GCP API Gateway is a collection of OpenAPI specifications and related settings that define the behavior of the API. We provide the `openapi_documents` parameter with the path to the OpenAPI spec file that describes our AI API.

- We set up an IAM policy for our API Config using the `ApiConfigIamPolicy` resource named `api-config-iam-policy`. In this policy, we specify who has the role of `apigateway.invoker`, which allows invocation of the API. For the sake of an example, we're granting this role to `someone@example.com`.

- Lastly, we export the URL of our API Gateway for easy access to the endpoint once it's deployed.

This setup provides a layer of security using IAM roles, thereby ensuring that only authorized users and services can invoke your AI API endpoints.