Uniform Access Control across AI/ML Environments

Question

Pulumi · Accepted Answer

Uniform access control across AI/ML environments is crucial for ensuring that only authorized entities can interact with the machine learning infrastructure and assets, such as datasets, models, and pipelining resources. Typically, access control is implemented using Identity and Access Management (IAM) services provided by the cloud provider hosting the AI/ML environment.

In this example, we will create access control policies for AI/ML resources in both Google Cloud and Azure. To do this, we will use the Pulumi Google Cloud Provider and Pulumi Azure Provider to manage IAM policies for a Google Cloud AI Platform Model and an Azure Machine Learning Workspace.

Here is a program written in Python using Pulumi which does the following:
1. Sets up an IAM policy for a Google Cloud AI Platform Model that allows specific members to have roles such as viewer, editor, etc.
2. Sets up access control for an Azure Machine Learning Workspace to manage which Azure Active Directory identities have access.

```python
import pulumi
import pulumi_azure_native as azure_native
import pulumi_azure_native.machinelearningservices as mls
import pulumi_google_native as google_native

# Configure access control for an Azure Machine Learning Workspace
azure_ml_workspace_name = 'myMachineLearningWorkspace'
resource_group_name = 'myResourceGroup'

# Assign a role to a user, group, or service principal for the Azure ML Workspace
workspace_connection = mls.WorkspaceConnection(
    "workspaceConnection",
    account_name=azure_ml_workspace_name,
    properties=mls.ConnectionPropertiesArgs(
        auth_type="PAT",
        value="connectionValue",
        target="targetResource",
    ),
    resource_group_name=resource_group_name,
    workspace_name=azure_ml_workspace_name
)

# Configure IAM for a Google Cloud AI Platform Model
project_id = 'my-gcp-project'
model_id = 'myModel'

# Define the IAM policy bindings for the model
bindings = [{
    "role": 'roles/ml.admin',  # Example role
    "members":[
        'user:example-user@gmail.com',
    ]
}]

# Apply the IAM policy to the model
model_iam_policy = google_native.ml.v1.ModelIamPolicy(
    "modelIamPolicy",
    project=project_id,
    modelId=model_id,
    bindings=bindings,
)

pulumi.export('Azure ML Workspace Connection ID', workspace_connection.id)
pulumi.export('Google Cloud Model IAM Policy', model_iam_policy.bindings)
```

In this program, we first define access control for an Azure ML Workspace by creating a `WorkspaceConnection` resource. We are establishing a connection based on a Personal Access Token (PAT) with the properties required, including the `auth_type`, `value` for the token, and the `target` resource. This would be where you specify which Azure Active Directory identities have access to the Machine Learning Workspace.

Next, we created an IAM policy using the `ModelIamPolicy` resource for a Google Cloud AI Platform Model. In the bindings, we specify the role and the members (e.g., users or service accounts) that are assigned that role. Here the role `roles/ml.admin` is given to a hypothetical user `example-user@gmail.com`. You can change the role to match your requirements, for instance, `roles/ml.viewer` for a view-only access.

At the end of the program, we export the identifiers for the created resources – the `WorkspaceConnection` ID for Azure and the IAM policy bindings for the Google Cloud Model. These exports make it easy to reference these resources outside of Pulumi, such as in scripts or other Pulumi stacks.

This code illustrates basic access control configuration in both clouds. However, real-world scenarios might require more granular controls, including additional roles and conditions. It's also essential to manage your service accounts and user identities separately and securely.

Remember, managing access to cloud resources is a sensitive operation that should only be performed by users with the necessary permissions and understanding of the cloud provider's IAM services. Always review and test IAM policies in a safe environment before deploying them to production to ensure they behave as expected.