Conditional Access for Azure Machine Learning Workspaces

Question

Pulumi · Accepted Answer

Conditional Access is a feature in Azure Active Directory (Azure AD) that allows you to enforce policies that require users to meet certain criteria before they can access specific resources. This is important for ensuring that only the right individuals in the right context can access your Azure Machine Learning workspaces, thus improving security.

In Pulumi, you can set up Conditional Access Policies for Azure Machine Learning workspaces by using the `azuread` provider. This typically involves creating policies that define the conditions under which access is granted or denied to your workspace.

The following program demonstrates how to create a Conditional Access Policy for an Azure Machine Learning workspace using Pulumi and Python. This policy aims to ensure that only members of a specified group can access the workspace from a particular set of locations.

Please note that this program assumes you have already configured your Pulumi environment for Azure and have the necessary permissions to create resources within your Azure AD tenant.

```python
import pulumi
import pulumi_azuread as azuread

# Define a Conditional Access Policy
conditional_access_policy = azuread.ConditionalAccessPolicy("example-policy",
    # State of the policy, "enabled" to enforce the policy, "disabled" to turn it off
    state="enabled",
    # Display name for the policy
    displayName="Access Policy for Azure ML Workspace",
    # Define who the policy targets
    conditions=azuread.ConditionalAccessPolicyConditionsArgs(
        users=azuread.ConditionalAccessPolicyConditionsUsersArgs(
            # Include users by role, user ID, or group ID
            includedUsers=["group-id-to-include"],
            # Exclude users by role, user ID, or group ID
            excludedUsers=["user-id-to-exclude"],
        ),
        # Define locations from which users can access 
        locations=azuread.ConditionalAccessPolicyConditionsLocationsArgs(
            includedLocations=["All"],
            # Optionally exclude certain locations
            # excludedLocations=["excluded-location-id"]
        ),
    ),
    # Control settings, like grant controls - which specifies what's required to satisfy policy (e.g., MFA, compliant device)
    grantControls=azuread.ConditionalAccessPolicyGrantControlsArgs(
        # Require multi-factor authentication 
        builtInControls=["mfa"],
        # Set the condition operator, "OR" or "AND", though here we're just using one control
        operator="OR",
    ),
    # Optionally, set session controls like sign-in frequency, and persistent browser session settings
    # sessionControls=...
)

# Export the ID of the Conditional Access Policy
pulumi.export('conditional_access_policy_id', conditional_access_policy.id)

# To use this Conditional Access Policy with an Azure Machine Learning workspace,
# you would associate the policy with the Azure AD group that has access rights to the workspace.
# The specifics of this association depend on how your Azure ML workspace and its permissions are set up.
```

Explanation of Resources:

- `ConditionalAccessPolicy`: Represents a Conditional Access policy in Azure AD.
  - `state`: The state of the policy, either "enabled" or "disabled".
  - `displayName`: User-friendly name of the policy.
  - `conditions`: Criteria that must be met for the policy to apply. Here, it is the users who are included and potentially locations.
  - `grantControls`: What requirements must be satisfied for access to be granted.

In the `grantControls`, we're currently requiring multi-factor authentication (`mfa`). The `operator` is set to `"OR"` meaning any of the grant controls would be sufficient, but since there's only one it does not matter much here.

Keep in mind that to fully integrate Conditional Access with your Azure Machine Learning workspace, you would need to align the Conditional Access policies with user permissions and roles that are granted access to the workspace. These details are specific to how your Azure AD is organized and your workspace's role-based access controls (RBAC).

Make sure to replace `"group-id-to-include"`, `"user-id-to-exclude"`, and other placeholder values with actual IDs relevant to your organization. Also, in a real-world scenario, you would want to refine the conditions further, potentially excluding specific locations or requiring compliant devices.