1. Multi-Tenant AI Applications with Auth0 User Segmentation

    Python

    Multi-tenant applications are designed to isolate data and configuration for different tenants or users that may share the same application instance. Auth0 is a popular authentication and authorization platform that supports multi-tenancy through its features like organizations, roles, and permissions.

    In the context of AI applications, user segmentation with Auth0 might involve setting up different environments for each tenant so they can access only their data and AI models. You'd typically create unique roles and permissions for each tenant to ensure that they have the right level of access to the application's features and data.

    To configure a multi-tenant AI application with user segmentation using Auth0, you can define resources like auth0.Tenant, auth0.User, and auth0.Organization. Each of these plays a role in the multi-tenancy setup:

    • auth0.Tenant: This is the root level configuration that contains settings that apply to your Auth0 tenant. For a multi-tenant application, you might have a single Auth0 tenant that serves multiple customer organizations.
    • auth0.User: A user resource represents a user in your application that can be assigned to one or more organizations or roles, allowing for fine-grained access control.
    • auth0.Organization: Represents an organization within Auth0, which can be used to group users and implement multi-tenancy.

    Below is a program that sets up an Auth0 tenant configuration, creates an organization that represents a tenant within our AI app, and a user who is a member of that organization. These resources help segment users and ensure they only have access to their respective data and models.

    import pulumi import pulumi_auth0 as auth0 # Define an Auth0 tenant resource. # This is normally setup once and usually not included in the day-to-day IaC operations. # It is included here for completeness. auth0_tenant = auth0.Tenant("my-tenant", # Various flags and settings for the tenant. # For more details on each setting, refer to the Auth0 documentation: # https://www.pulumi.com/registry/packages/auth0/api-docs/tenant/ flags={ "enable_apis_section": True, "enable_client_connections": True, "enable_pipeline2": True, }, # Custom error page configuration for the tenant. error_page={ "html": "<html><body><h1>Custom Error Page</h1></body></html>", "show_log_link": False, "url": "https://my-custom-error-page.example.com", }, # Custom branding settings. friendly_name="My AI Application", support_email="support@example.com", support_url="https://support.example.com", ) # Define an Auth0 organization resource. # Organizations are used to represent a tenant (customer) of your multi-tenant AI application. ai_org = auth0.Organization("ai-org", name="ai-organization", display_name="AI Organization", # Branding for the organization. branding={ "logo_url": "https://my-org-logo.example.com/logo.png", "colors": { "primary": "#0000FF", }, }, # Additional metadata can be attached to an organization as needed. metadata={ "tenant_id": "tenant12345", }, ) # Define an Auth0 user resource and associate them with the organization. # This user will belong to the AI Organization and have access to its AI models. tenant_user = auth0.User("tenant-user", connection_name="Username-Password-Authentication", # Connection to use for authentication. email="user@example.com", password="very-strong-password", # In a real application, enforce strong passwords. user_metadata={ "role": "data-scientist", # Custom metadata that can be used by the application. }, # Email should be verified for security reasons. email_verified=True, # Associating the user with the 'ai-organization'. app_metadata={ "organization_id": ai_org.id, }, ) # Export the IDs of the created resources. pulumi.export("tenant_id", auth0_tenant.id) pulumi.export("organization_id", ai_org.id) pulumi.export("user_id", tenant_user.user_id)

    In the above program:

    • We start by declaring an auth0.Tenant to hold global configurations. While tenants are usually set up once and rarely changed, this resource is shown for completeness.
    • We create an auth0.Organization resource called ai-org for organizational segmentation. Creating an organization allows us to represent and manage separate tenants within our AI application.
    • An auth0.User resource is created to represent an individual user. This user is associated with the organization through the app_metadata property. The user_metadata can store custom attributes such as roles or permissions specific to your application.

    This program initializes Auth0 multi-tenancy for our AI application, but it's just the beginning. We haven't created any role-based access control (RBAC) rules, machine to machine (M2M) applications, APIs, or additional user permissions that are also essential parts of a secure multi-tenant AI application. Those would be set up based on your specific needs using additional Auth0 resources.


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