Implementing Hierarchical Permissions for AI Teams in GCP

Question

Pulumi · Accepted Answer

To implement hierarchical permissions for AI teams on Google Cloud Platform (GCP) using Pulumi, we'll leverage IAM (Identity and Access Management) policies at various levels of the GCP resource hierarchy. GCP organizes resources hierarchically in the following manner:

1. Organization - The top-level container that represents your company. IAM policies here apply across all GCP resources in your organization.
2. Folders - Subcontainers that can represent different departments, teams, or environments within your organization.
3. Projects - Projects are the containers for resources and services, and IAM policies at this level are more specific.
4. Resources - The most specific level, such as Compute Engine instances, Cloud Storage buckets, or AI Feature Stores, where permissions can be finely tuned.

By setting IAM policies at different levels of this hierarchy, you can effectively manage permissions across your teams and services. For example, you might grant broad roles at the organization level (like Organization Viewer) and more specific roles at the resource level (like AI Feature Store Editor).

We will be creating a Pulumi program in Python that outlines how to use the Pulumi GCP provider to set hierarchical IAM policies for an AI Feature Store. The example will include setting organization-level and resource-level policies.

Here is a Python program using Pulumi to set up hierarchical permissions:

```python
import pulumi
import pulumi_gcp as gcp

# Organization level IAM policy
# Assuming you already have the Organization ID.
# Here you are setting up a policy that grants the role of 'roles/resourcemanager.organizationViewer'
# This role allows members to view the organization's display name and asset inventory information.
org_iam_policy = gcp.cloudresourcemanager.OrganizationIAMPolicy("org-iam-policy",
    bindings=[{
        "members": ["user:alice@example.com"],
        "role": "roles/resourcemanager.organizationViewer",
    }],
    etag="*",  # To ensure you're overwriting any previous policies, use a wildcard ETag.
    organization="your-organization-id")

# Project level IAM policy
# Here you are setting up a policy that grants the role of 'roles/editor' to `user:bob@example.com`
# for a specific project.
project_iam_policy = gcp.cloudresourcemanager.ProjectIAMPolicy("proj-iam-policy",
    bindings=[{
        "members": ["user:bob@example.com"],
        "role": "roles/editor",
    }],
    etag="*",
    project="your-project-id")

# AI Feature Store IAM Binding
# This sets a resource-level policy for an AI Feature Store, granting the role of 'roles/aiplatform.viewer'
# to `group:data-scientists@example.com`.
feature_store_iam_binding = gcp.vertex.AIFeaturestoreIamBinding("ai-featurestore-iam-binding",
    featurestore="your-featurestore-id",
    members=["group:data-scientists@example.com"],
    role="roles/aiplatform.viewer",
    project="your-project-id",
    region="your-featurestore-region")

# Exporting the IAM policy IDs to retrieve them easily
pulumi.export("org_iam_policy", org_iam_policy.id)
pulumi.export("project_iam_policy", project_iam_policy.id)
pulumi.export("feature_store_iam_binding", feature_store_iam_binding.id)
```

In this program, you're setting IAM policies at different levels of the GCP resource hierarchy:

1. At the organization level, for broad organization-wide permissions.
2. At the project level, for project-specific permissions.
3. At the resource level, for AI Feature Store-specific permissions.

Make sure to replace `your-organization-id`, `your-project-id`, `your-featurestore-id`, and `your-featurestore-region` with actual IDs and regions from your GCP environment. Note that the members you specify (like `alice@example.com`, `bob@example.com`, and `data-scientists@example.com`) must be actual Google user accounts or Google Groups in your organization.

This program demonstrates the hierarchical structure of permissions in GCP and how you can manage them using Pulumi. Remember to run `pulumi up` to deploy your changes to GCP after setting up your Pulumi stack and GCP credentials.