Configuring IAP for Secure AI Dashboards on GKE

Question

Pulumi · Accepted Answer

Configuring Identity-Aware Proxy (IAP) for securing AI Dashboards on Google Kubernetes Engine (GKE) involves several steps, which includes setting up a GKE cluster, configuring IAP, and then deploying your AI dashboards with the appropriate access controls in place.

First, let's discuss the resources we'll be using:

GKE Cluster: This is the Kubernetes environment where your AI Dashboard applications will be running.
IAP IAM Member: IAP uses Google Cloud IAM (Identity and Access Management) to control who can access your cluster. You define who can access your dashboard by creating IAM policies.
Backend Service: This is the component that IAP will be protecting. The AI Dashboard will be running as a service within the GKE.

Here's a high-level program in Python using Pulumi to provision these resources. This program will guide you through the steps of creating a GKE cluster, configuring IAP access, and setting up backend services.

First, make sure you have Pulumi installed and configured with credentials for Google Cloud.

Remember to replace placeholders (like <your_project>, <your_cluster_name>, etc.) with actual values specific to your setup.

import pulumi
import pulumi_gcp as gcp

# Create a GKE cluster.
cluster = gcp.container.Cluster("ai-dashboard-cluster",
    initial_node_count=1,
    node_version="latest",
    min_master_version="latest",
    node_config={
        "machine_type": "n1-standard-1",
        "oauth_scopes": [
            "https://www.googleapis.com/auth/cloud-platform"
        ],
    },
)

# The email of the IAM user/service account.
iam_user_email = "user:someone@example.com"

# Define the IAP IAM member for the backend service.
iap_iam_member = gcp.iap.WebBackendServiceIamMember("iap-ai-dashboard-iam",
    project="<your_project>",
    backend_service="<backend_service_id>",
    role="roles/iap.httpsResourceAccessor",
    member=pulumi.Output.concat("user:", iam_user_email),
)

# Export the cluster name and endpoint to access later on.
pulumi.export("cluster_name", cluster.name)
pulumi.export("cluster_endpoint", cluster.endpoint)

This Pulumi program will provision a new GKE cluster and configure IAP for a backend service. In the iap_iam_member section, we’re assigning an IAM role roles/iap.httpsResourceAccessor to a specific user that will allow them to access resources behind IAP. The backend_service is a placeholder that should be replaced with the actual ID of your backend service where the AI Dashboard is running.

As the GKE cluster is being created, Pulumi outputs the cluster name and endpoint which can be helpful for further configuration or debugging.

Please replace the placeholders with the appropriate values for your project. After running this Pulumi code, your Kubernetes cluster will be up, and the specified user will have access to the AI Dashboard through IAP.

Keep in mind that this program does not cover the deployment of the AI Dashboard itself or the detailed configuration of IAP, which will vary depending on the specifics of your application and environment.