Protected Machine Learning Web Interfaces with IAP

Question

Pulumi · Accepted Answer

Protecting machine learning (ML) web interfaces with Identity-Aware Proxy (IAP) benefits your ML systems by adding a security layer that controls who can access them. This is particularly important when deploying sensitive applications like ML models that should not be accessible by the general public.

In a Google Cloud Platform (GCP) environment, we can deploy an ML application on a Compute Engine instance and use IAP to securely manage access to it. The following steps and accompanying Pulumi Python program demonstrate how to set this up:

1. Enable the necessary GCP services, like Compute Engine and IAP.
2. Create a GCP Compute Engine instance to host the ML web interface.
3. Deploy the ML application on the Compute Engine instance.
4. Configure IAP to protect the ML web interface, including setting up OAuth credentials and setting IAM policies for the authorized users.

For the purposes of this demonstration, we'll skip steps 2 and 3 (which would be specific to the ML application being deployed) and focus on step 1 and step 4 with the following program:

- We'll create an `OAuth2 client` used for IAP.
- We'll set IAP IAM policies to specify who has access.

Please ensure you have GCP credentials configured for Pulumi prior to running the following program. Here's the Pulumi Python program that automates this setup:

```python
import pulumi
import pulumi_gcp as gcp

# Enable the necessary services for IAP and Compute Engine.
services = [
    "iap.googleapis.com",
    "compute.googleapis.com"
]
for service in services:
    gcp.projects.Service(f'enable-{service}', service=service)

# Replace these variables with your project's specific details.
project = gcp.config.project
brand_name = "my-brand"
oauth_client_name = "my-oauth-client"

# Create a Brand for IAP, which represents how your application appears to users.
brand = gcp.iap.Brand("brand",
    support_email="support@example.com",  # Replace with your support email.
    application_title=brand_name)

# Create an OAuth2 client that will be used by IAP.
oauth_client = gcp.iap.Client("oauthClient",
    brand=brand.name,
    display_name=oauth_client_name)

# Provide the IAP OAuth2 client ID and secret for your application configuration.
pulumi.export('iap_oauth_client_id', oauth_client.client_id)
pulumi.export('iap_oauth_client_secret', oauth_client.secret)

# IAM policy to grant access to the IAP-secured resources.
# Replace `user_email` with the email of the user you want to grant access to.
user_email = "user@example.com"
web_iam_policy_member = gcp.iap.WebTypeComputeIamMember("webIamPolicyMember",
    project=project,
    member=f"user:{user_email}",
    role="roles/iap.httpsResourceAccessor")

# If required, you can add more users or service accounts by creating additional
# WebTypeComputeIamMember instances with the respective member definitions.
```

This program sets up the necessary OAuth2 client within the IAP brand and grants a user access to secured resources through the IAM policy for a web-type Compute Engine resource. The `iap_oauth_client_id` and `iap_oauth_client_secret` are exported so that you can use them to configure your ML application to work with IAP. The IAM policy, applied with the `WebTypeComputeIamMember` resource, controls who can access the IAP-secured resources by specifying member roles.

When you run this program with the `pulumi up` command, it will create the IAP brand and OAuth2 client, outputting the generated client ID and secret, and set the IAM policies for the specified user. This means your ML web interface will be protected by IAP, allowing only authenticated users to access it.

Keep in mind that a brand is a top-level container for IAP resources in your GCP project. Typically, you have only one brand for each project. Additionally, the OAuth2 client created here is what your ML web interface will use to interact with IAP.

You might need to perform additional configuration inside your ML web application to integrate properly with IAP, such as handling the authentication flow and verifying IAP-signed headers. The details would depend on the specifics of your ML application and its web framework.