Protected Model Endpoints via GCP IAP

Question

Pulumi · Accepted Answer

To create protected model endpoints on Google Cloud Platform (GCP) using Identity-Aware Proxy (IAP), we can utilize Pulumi to automate the configuration of the necessary resources. Here's an overview of the steps we'll follow:

1. Set up an OAuth consent screen in GCP, which will be used to display consent to the users if the application is accessed by external users. This is done by creating a `Brand` resource, which represents the OAuth consent screen configuration.

2. Define a backend service that will host your model, and this can be a Compute Engine, a Kubernetes Engine, or an App Engine service.

3. Enable IAP for this service so that requests to the model's endpoint require authentication. This is done by configuring the IAP WebBackendService or IAP Tunnel (depending on your architecture).

4. Assign the necessary IAM policies and roles to control who has access to your model's endpoint via IAP.

Let's write a Pulumi program to accomplish step 3 and 4 where we have assumed steps 1 and 2 have been completed (since they typically involve GUI-based configurations that are out of scope for a Pulumi automation script).

### Pulumi Program for GCP IAP-protected Endpoints

The following program demonstrates how to configure IAP for a Cloud Run service (as an example for step 2) and set up access controls (as an example for step 4). Cloud Run is chosen for demo purposes since it is a common platform for deploying containerized applications, which could host a model API.

Make sure you replace `"PROJECT_ID"`, `"MEMBER"` and other placeholders with your specific GCP project identifier and member identifiers.

```python
import pulumi
import pulumi_gcp as gcp

# Enable IAP for a Cloud Run service
# The Cloud Run service must be deployed beforehand; here we assume the service
# name is 'my-protected-model'

# Retrieve the Cloud Run service ID for use in IAP activation
cloud_run_service = gcp.cloudrun.Service.get(
    "my-cloud-run-service",
    "projects/PROJECT_ID/locations/global/services/my-protected-model"
)

# Enable IAP for the retrieved Cloud Run Service
iap_web = gcp.iap.WebBackendServiceIamPolicy("iap-web",
    project="PROJECT_ID",
    web_backend_service=cloud_run_service.id,
    policy_data=pulumi.Output.all(cloud_run_service.iam_policy).apply(
        lambda policy: json.dumps({
            "bindings": [
                {
                    "role": "roles/iap.httpsResourceAccessor",
                    "members": ["user:MEMBER"],
                },
            ],
        })
    ),
)

# IAM bindings for IAP-protected Cloud Run service
# Define who can access the Cloud Run service with IAP
iap_binding = gcp.iap.WebIamBinding("iap-binding",
    project="PROJECT_ID",
    role="roles/iap.httpsResourceAccessor",
    members=["user:MEMBER"],  # Replace 'MEMBER' with actual member IDs, e.g. "user:test@example.com"
)

# Export the IAP web endpoint URL
pulumi.export('iap-web-url', cloud_run_service.statuses[0].url)
```

The above program creates IAM policy bindings for the Identity-Aware Proxy with a given member. The program then exports the Cloud Run service URL. Requests to this URL will be intercepted by IAP for user authentication and authorization.

**Note:** This is a simplification for the sake of illustration. Your actual Pulumi program might need to include additional security checks, more IAM roles and permissions, and error handling, among other aspects according to your specific requirements.