1. Conditional Access Policies for AI Data Scientists


    Conditional Access Policies are a security feature that allows organizations to implement automated access control decisions for accessing cloud apps, based on conditions. For AI data scientists working in the Azure environment, setting up Conditional Access Policies is vital to secure access to AI resources and services.

    In the context of Pulumi and Azure, you can define these policies programmatically using the azuread Pulumi provider. Below is a Pulumi Python program that creates a Conditional Access Policy for AI data scientists. This policy can enforce multi-factor authentication (MFA) or restrict access based on user risk level, application, location, and device state.

    Before running this program, make sure you have installed the pulumi CLI and the pulumi-azuread plugin, and that you have an authenticated Azure session.

    Here's what the program does, step by step:

    1. It imports the necessary Pulumi Azure Active Directory (AzureAD) module.
    2. Declares a Conditional Access Policy that specifies which users it applies to, the cloud applications it protects, and the conditions under which it triggers.
    3. The conditions include requirements for multi-factor authentication and specific user risk levels.
    4. The policy is then applied, requiring multi-factor authentication for any AI data scientist role when signing in to the specified AI applications.

    Let's see the Pulumi program:

    import pulumi import pulumi_azuread as azuread # This is the Conditional Access Policy resource definition. conditional_access_policy = azuread.ConditionalAccessPolicy("aiDataScientistsPolicy", # Display name for the Conditional Access Policy. display_name="AI Data Scientists Conditional Access Policy", # The state of the policy. Can be "enabled", "disabled", or "enabledForReportingButNotEnforced". state="enabled", # Defining the conditions under which the policy will apply. conditions=azuread.ConditionalAccessPolicyConditionsArgs( # Applications that the policy applies to. applications=azuread.ConditionalAccessPolicyConditionsApplicationsArgs( included_applications=["All"], excluded_applications=["00000002-0000-0000-c000-000000000000"], # Exclude the Azure portal ), # Users, groups, and roles the policy applies to. users=azuread.ConditionalAccessPolicyConditionsUsersArgs( included_users=["ai-data-scientist"], # Assuming a group exists that includes AI data scientists excluded_users=["00000000-0000-0000-0000-000000000000"], # Exclude directory roles ), # Client app types the policy applies to. client_app_types=[ "browser", "mobileAppsAndDesktopClients", ], # Locations that the policy applies to. locations=azuread.ConditionalAccessPolicyConditionsLocationsArgs( included_locations=["All"], excluded_locations=["00000000-0000-0000-0000-000000000000"], # Trusted locations can be excluded ), ), # Controls that enforce the policy. grant_controls=azuread.ConditionalAccessPolicyGrantControlsArgs( operator="OR", built_in_controls=[ "mfa", # Require multi-factor authentication ], ), # The session control settings for the policy. session_controls=azuread.ConditionalAccessPolicySessionControlsArgs( # For example, sign in frequency might require re-authentication every 1 hour. sign_in_frequency=1, sign_in_frequency_period="hours", ), ) # After defining the policy, you can export the ID of the policy for reference. pulumi.export("conditional_access_policy_id", conditional_access_policy.id)

    Here's what each part of the code does:

    • conditional_access_policy: The main resource that represents the Conditional Access Policy you are creating.
    • display_name: The name of the policy, displayed in the Azure portal.
    • state: The policy's state, which could be 'enabled', 'disabled', or 'enabledForReportingButNotEnforced', depending on your needs.
    • conditions: Define the conditions that trigger the policy. These include applications, users, client app types, and locations.
    • grant_controls: Define the controls that enforce the policy, such as multi-factor authentication.
    • session_controls: Define session-related settings.

    Once you run this program with pulumi up, it will provision the AzureAD Conditional Access Policy as defined. Remember to customize the included_users and other parameters as per your organizational structure and security requirements.

    Make sure to test any Conditional Access Policies in a non-production environment first to ensure they behave as expected and don't disrupt legitimate user access.