Fine-Grained Permissions for Machine Learning Services Using Azure Role Definitions

Question

Pulumi · Accepted Answer

Fine-grained permissions in Azure are typically accomplished by creating custom role definitions. These role definitions specify a set of permissions that can be applied to users, groups, or identities, allowing you to control who can perform what operations on your resources.

In the context of Azure Machine Learning services, you might use custom role definitions to control access to various aspects of the service, such as experiments, models, deployments, and datastores.

Below is a Pulumi program in Python that demonstrates how to create a custom role definition for Azure Machine Learning services. This role definition will give a user or principal the ability to read and list everything but restrict write access only to specific operations.

Before we dive into the code, you must have the Azure provider configured with Pulumi and have permissions to create role definitions in Azure Active Directory.

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a custom role definition for Azure Machine Learning services.
custom_role_definition = azure_native.authorization.RoleDefinition(
    "customMLRoleDefinition",
    role_definition={
        "roleName": "Custom Machine Learning Role",
        "description": "Allows for fine-grained access control to Azure Machine Learning resources.",
        # Assignable scopes define the scope of the role. A global scope "/" means it can be assigned at any scope.
        "assignableScopes": ["/"],
        "permissions": [{
            # Actions pattern follows Azure's Resource Provider operations. Refer to the official docs for specific strings.
            "actions": [
                "Microsoft.MachineLearningServices/workspaces/read",
                "Microsoft.MachineLearningServices/workspaces/experiments/read",
                "Microsoft.MachineLearningServices/workspaces/models/read",
                "Microsoft.MachineLearningServices/workspaces/endpoints/read",
                "Microsoft.MachineLearningServices/workspaces/deployments/read",
                "Microsoft.MachineLearningServices/workspaces/datastores/read",
                # Add any specific write permissions you want to allow
                "Microsoft.MachineLearningServices/workspaces/experiments/write",
            ],
            # NotActions can be used to exclude certain actions even if they are included in the "actions" array above.
            "notActions": [],
        }],
        # Data actions are used to grant access to data-plane operations. You can specify them if needed.
        "dataActions": [],
        "notDataActions": [],
    })

# We are only defining the role here. To assign it to a user, group, or service principal, you would create a RoleAssignment
# You'll need the principal ID and the role definition ID from the role you've created.

# Export the ID of the new role definition so it can be used to assign to principals.
pulumi.export('role_definition_id', custom_role_definition.id)
```

This program defines a custom role that has read access to several types of Azure Machine Learning resources and write access to experiments. The permissions and scope you set for your role definition will depend on your specific use case and security requirements.

The following code snippet is an example (not part of the above program) of how you might assign this role to a principal —a user, a group, or a service principal— that you want to have these permissions:

```python
role_assignment = azure_native.authorization.RoleAssignment(
    "customMLRoleAssignment",
    scope="/",
    role_definition_id=custom_role_definition.id,
    principal_id="principal-guid-you-want-to-assign-role-to" # Replace with actual principal ID
)
```

Replace `"principal-guid-you-want-to-assign-role-to"` with the actual object ID of the Azure AD user, group, or service principal to whom you want to assign the custom role. The `scope` argument assigns the role at the specified scope. In this case, it is the root scope, meaning this role can be assigned across the entire Azure tenancy. You will likely want to restrict this to a narrower scope, such as a specific subscription, resource group, or even a specific resource.

For more information on the exact permission strings and structure, you can refer to Azure's official documentation on [Role definitions](https://docs.microsoft.com/en-us/azure/role-based-access-control/role-definitions) and [Resource Provider operations](https://docs.microsoft.com/en-us/azure/role-based-access-control/resource-provider-operations).