Configuring Access Control for GCP AI Notebooks Instances

Question

Pulumi · Accepted Answer

To configure access control for Google Cloud AI Platform Notebooks (referred to as AI Notebooks), you'll need to manage the Identity and Access Management (IAM) policies for the service. This involves specifying which Google Cloud accounts can access, create, or administer these notebook instances.

To implement access control, you typically assign roles to Google Cloud identities (users, service accounts, etc.) at different levels of the resource hierarchy (such as project level, folder level, or organization level). There are predefined roles for AI Notebooks, like `roles/notebooks.admin`, which grants full access to AI Notebooks resources, or `roles/notebooks.viewer`, which allows read-only access.

Below is a Pulumi program that demonstrates how to set access control for an AI Notebooks instance in GCP. The program uses the `pulumi_gcp` provider to define the IAM policy for a given project and service account. In this example, we assume that the AI Notebooks instance already exists, and we're modifying the IAM policy to grant particular roles to specific members:

```python
import pulumi
import pulumi_gcp as gcp

# Replace these values with the appropriate project ID, member identity, and role.
project_id = "your-gcp-project-id"
member_identity = "user:john.doe@example.com"  # This could be a user, service account, group, etc.
role = "roles/notebooks.admin"  # This could be any role that is appropriate for your needs.

# Define the IAM policy for our AI Notebooks instance.
ai_notebooks_iam_policy = gcp.projects.IAMPolicy("ai-notebooks-iam-policy",
                                                 project=project_id,
                                                 bindings=[
                                                     gcp.projects.IAMPolicyBindingArgs(
                                                         role=role,
                                                         members=[member_identity]
                                                     )
                                                 ])

# Export the project ID and member identity, which can be used for debugging or introspection.
pulumi.export("project", project_id)
pulumi.export("member", member_identity)
```

In the program above, we use `gcp.projects.IAMPolicy` to set an IAM policy binding for the project. The IAM policy is defined by providing:

- `project`: The project ID of the GCP project.
- `bindings`: A list of IAM policy bindings, each represented by `gcp.projects.IAMPolicyBindingArgs`. Each binding requires:
  - `role`: The role that you want to grant. This should correspond to one of the AI Notebooks roles available in GCP.
  - `members`: A list of members to whom the role should be applied. This is typically a user email, but could also be a service account, Google group, or domain.
  
Finally, the program exports the `project_id` and `member_identity` to provide visibility into which project and member the IAM policy has been applied. This is useful for debugging purposes.

When you run this Pulumi program, it will apply the IAM policy defined in the code to your GCP project, thus configuring the access control for your AI Notebooks instance.

Please ensure that the project ID and member identity are edited to reflect your GCP project's actual ID and the identity to which you want to grant access. Additionally, modify the `role` if needed to better reflect the desired access level for your use case.

Remember that managing access control via IAM policies should be performed carefully to avoid inadvertently granting excessive permissions, which might lead to security vulnerabilities. Always adhere to the principle of least privilege by granting only the minimal set of permissions necessary to perform a job.