1. Multi-factor Authentication for AI Dashboards with Auth0


    Multi-factor authentication (MFA) is a security system that requires more than one method of authentication from independent categories of credentials to verify the user's identity for a login or other transaction. It is a core component of a strong identity and access management (IAM) policy.

    Using Auth0, a platform for authentication and authorization, you can add MFA to your applications with ease. Within Auth0, various types of MFA can be implemented such as SMS, TOTP (Time-Based One-Time Password), Push notifications, and more.

    For the purpose of this example, I'll guide you through setting up MFA for an AI Dashboard using Pulumi. The exact configuration details would depend on your specific requirements, but the general approach involves these steps:

    1. Define Auth0 Client which represents the AI Dashboard application in Auth0.
    2. Configure Rules, Hooks, or Actions to enforce MFA whenever a user attempts to log in to the AI Dashboard.
    3. Create Prompt settings to customize the MFA experience for your users.

    Let's assume your AI Dashboard requires users to log in, and you want to enforce MFA for each login attempt. Below is a Pulumi Python program that sets up a simplified version of this authentication flow using Auth0.

    import pulumi import pulumi_auth0 as auth0 # Create an Auth0 Client for the AI Dashboard application. ai_dashboard_client = auth0.Client("ai-dashboard-client", name="AI Dashboard", # Replace with your application type, callback URLs, etc. app_type="regular_web", callbacks=["https://your-dashboard-domain/callback"], grant_types=["authorization_code", "refresh_token"], allowed_logout_urls=["https://your-dashboard-domain"], web_origins=["https://your-dashboard-domain"], oidc_conformant=True, # The client secret is automatically managed by Auth0, but you may need to handle it according to your security procedures. ) # To enable MFA, you usually create a Rule in Auth0. # A rule is JavaScript code that is executed in Auth0's extensible authorization pipeline. mfa_rule = auth0.Rule("mfa-rule", script="""function (user, context, callback) { // Require MFA for every user context.multifactor = { provider: 'any', allowRememberBrowser: false }; callback(null, user, context); }""", order=1, # Defines the order of execution if you have other rules. enabled=True, # The rule is activated by setting enabled to true. # The rule applies to the AI Dashboard client. conditions=[auth0.RuleConditionArgs( client_id=ai_dashboard_client.client_id, )], ) # Export the AI Dashboard client ID and domain to be used in the application. pulumi.export("ai_dashboard_client_id", ai_dashboard_client.client_id) pulumi.export("auth0_domain", ai_dashboard_client.domain)

    In this program:

    • We defined an Auth0 client representing our AI Dashboard application, providing necessary details like the application type and allowed URLs for callback and logout functionalities.

    • A rule named mfa-rule is created which enforces multi-factor authentication. Within the context of this rule, context.multifactor is used to define the MFA settings. In this case, we specify any to indicate that any available MFA provider can be used.

    • We export the AI Dashboard client ID and Auth0 domain because the application will need these to interact with Auth0's APIs (e.g., for logging in users).

    Remember to replace placeholder values with your actual application details and domains. Additionally, make sure to set callbacks, allowed_logout_urls, and web_origins according to the URLs where your application is hosted.

    Note: The actual implementation might be more complex based on your requirements. You might need to handle secrets securely, interact with APIs to further customize behavior, or set up additional resources within Auth0.

    For more in-depth information, visit the Auth0 documentation. This guide should give you an outline to start implementing MFA in your AI Dashboard application with Pulumi and Auth0.