1. Multi-Factor Auth for AI Model Training Dashboards with Okta

    Python

    To achieve multi-factor authentication (MFA) for an AI model training dashboard using Okta, you will create a few primary resources:

    1. Okta User: This represents a user in the Okta system that will access your AI Model Training Dashboard.
    2. Okta Group: This is a collection of users. A group can be given permissions to access the dashboard.
    3. Okta App OAuth: This is an OAuth application which represents your AI Model Training Dashboard within Okta. It is used to integrate your dashboard with the Okta authentication system.
    4. Okta Factor: This resource represents the MFA factor that will be added to the user for additional security.

    The following Pulumi program in Python will guide you through setting up these resources using the Okta provider. Before implementing this program, make sure you have:

    • An Okta organization where you can create these resources.
    • Pulumi CLI installed and configured with your Okta credentials.

    Here's the Pulumi Python program that sets up MFA for your dashboard:

    import pulumi import pulumi_okta as okta # Create an Okta Group for the AI Model Training Dashboard users ai_dashboard_group = okta.Group("aiDashboardGroup", name="AI Model Training Dashboard Users", description="Group for users of the AI Model Training Dashboard") # Create an Okta User who will access the AI Model Training Dashboard ai_dashboard_user = okta.User("aiDashboardUser", email="user@example.com", login="user@example.com", firstName="AI", lastName="User") # Assign the user to the group ai_dashboard_user_group_membership = okta.GroupMembership("aiDashboardUserGroupMembership", group_id=ai_dashboard_group.id, user_id=ai_dashboard_user.id) # Create an Okta OAuth Application to represent the AI Model Training Dashboard ai_dashboard_app = okta.app.OAuth("aiDashboardApp", label="AI Model Training Dashboard", type="web", grant_types=["authorization_code"], # The most common OAuth 2.0 grant type redirect_uris=["https://dashboard.example.com/oauth2/callback"], response_types=["code"], token_endpoint_auth_method="client_secret_basic", consent_method="TRUSTED", login_uri="https://dashboard.example.com/login", post_logout_redirect_uris=["https://dashboard.example.com/"]) # Define a Multi-Factor Authentication (MFA) factor for Okta user # In this case, we will setup a Google Authenticator factor mfa_factor = okta.Factor("mfaFactor", provider_id="google", user_id=ai_dashboard_user.id) # Optional: Set different policies or behaviors for MFA according to your needs # Output the necessary details pulumi.export("dashboard_app_client_id", ai_dashboard_app.client_id) pulumi.export("ai_dashboard_group_id", ai_dashboard_group.id) pulumi.export("ai_dashboard_user_id", ai_dashboard_user.id)

    In this program, you create a group for users who will have access to the AI Dashboard and add a user to this group. Subsequently, you define an OAuth application to represent your dashboard and specify its settings, like the callback URLs and allowed grant types.

    The program concludes with the creation of a Google Authenticator factor as the MFA solution for the user. The pulumi.export statements make some of the created resource IDs available outside of Pulumi for you to use elsewhere, like in your application or other automation tools.

    Please ensure you have the necessary permissions to create these resources within your Okta organization before running this program. It's also best practice to update the placeholders (like the user's email and the dashboard URIs) to reflect real values from your environment. After running the program with pulumi up, you will be able to see the outputs like the application client ID. Use these outputs to configure your dashboard application to integrate with Okta for authentication and MFA.


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