Machine Learning Dashboard Authentication with Auth0

Question

Pulumi · Accepted Answer

To set up a machine learning dashboard with authentication using Auth0, you need to integrate several components. First, Auth0 needs to be configured to handle user authentication. Then, the dashboard (hosted on a web service) should be secured with Auth0, ensuring that only authenticated users can access it.

Below is the outline of the Pulumi program written in Python that achieves the following:

1. Sets up Auth0 components to handle user authentication.
2. Generates the necessary configuration and resources required for Auth0.

**Auth0 Components Set up:**
- **Auth0 User**: Represent an end-user trying to authenticate in order to access the dashboard.
- **Auth0 Client**: A client in Auth0 parlance refers to an application making protected resource requests on behalf of the user.
- **Auth0 Connection**: A source of users for Auth0, it's how you set up Auth0's interaction with an identity provider.
- **Auth0 Rules**: A piece of JavaScript code that runs when the authentication process is executed. It can modify the returned tokens or enforce policies (like multifactor authentication).

I'll create a simple setup where:
- A new Auth0 user is created.
- A new Auth0 client (your machine learning dashboard) is defined.
- Auth0 connection, such as a database connection, is set up.
- Rules for the authentication process are defined.

**Please note**: The exact details of setting up Auth0 for your dashboard will depend on your specific use case, the technologies used in your dashboard, and how you've deployed it. You'll typically need to set up the client in Auth0, register the callback URLs, and integrate the Auth0 SDK into your dashboard application.

To run this Pulumi program, you'll need to have Pulumi CLI installed and configured with your state backend. You’ll also need to install the necessary Auth0 provider by running `pulumi plugin install resource auth0 vX.Y.Z` replacing `X.Y.Z` with the version of your choice.

Please replace the placeholders (`YOUR_DOMAIN`, `YOUR_CLIENT_ID`, etc.) with actual values from your Auth0 application settings.

**Here is the Pulumi program:**

```python
import pulumi
import pulumi_auth0 as auth0

# Note: These values should be replaced with your actual Auth0 Domain and Client ID.
domain = 'YOUR_DOMAIN'
client_id = 'YOUR_CLIENT_ID'

# Create an Auth0 user for the dashboard.
dashboard_user = auth0.User("dashboardUser",
    connection_name="Username-Password-Authentication", # The name of the database connection you want to use.
    email="user@example.com",
    password="password",  # In a real-world scenario, do not hardcode passwords!
    user_id="auth0|1234567890",  # This needs to be a unique ID.
    nickname="dashboardUser",
    email_verified=True,
    app_metadata={
        "roles": ["dashboard-user"]
    }
)

# Register a client (machine learning dashboard application) in Auth0.
dashboard_client = auth0.Client("dashboardClient",
    name="MachineLearningDashboard",
    grant_types=[
        "authorization_code",
        "refresh_token",
        "implicit",
        "password",
        "client_credentials"
    ],
    app_type="regular_web",
    callbacks=["https://YOUR_DASHBOARD_URL/callback"],  # The URL to your dashboard callback endpoint
    allowed_origins=["https://YOUR_DASHBOARD_URL"],  # The URL of your dashboard
    web_origins=["https://YOUR_DASHBOARD_URL"],  # The URL of your dashboard
    client_metadata={
        "dashboard": "machine-learning"
    }
)

# Create a database connection for the dashboard users in Auth0.
dashboard_connection = auth0.Connection("dashboardConnection",
    name="Machine-Learning-Dashboard-DB",
    strategy="auth0",
    options={
        "requires_username": False,
        "brute_force_protection": True,
        "import_mode": False,
        "disable_signup": False
    }
)

# Define an Auth0 Rule for the authentication process.
auth_rule = auth0.Rule("authRule",
    script="""
        function (user, context, callback) {
            // This is a JavaScript code snippet executed during the auth process.
            // For a real-world scenario, use it to implement additional authentication checks,
            // like MFA or checking membership in a certain group.
            
            // Here is a simple example where we add a claim for role:
            const namespace = 'https://YOUR_DASHBOARD_URL/';
            context.idToken[namespace + 'role'] = user.app_metadata.roles;
            callback(null, user, context);
        }
    """,
    order=1
)

# Exporting important URLs and identifiers that might be useful.
pulumi.export("dashboard_user_id", dashboard_user.user_id)
pulumi.export("dashboard_user_email", dashboard_user.email)
pulumi.export("dashboard_client_id", dashboard_client.client_id)    
```

**Explanation of the Pulumi Program:**

- **Auth0 User**: We create an Auth0 user with a username and password, which are the credentials the user will use to log into the dashboard.
- **Auth0 Client**: This defines your machine learning dashboard as an application in Auth0, allowing it to use Auth0 for authentication.
- **Auth0 Connection**: Establishes a means for Auth0 to authenticate users, in this case, a database of users managed by Auth0.
- **Auth0 Rule**: Defines a simple authentication rule that assigns the user's role from the application metadata to the `idToken`, which then can be used in your application for authorization decisions.

This setup only scratches the surface of what you can do with Auth0 and Pulumi. To get this working with your specific dashboard you would need to fill in your actual `YOUR_DOMAIN`, `YOUR_CLIENT_ID`, and `YOUR_DASHBOARD_URL` values and adjust the specific settings and rules to your application's needs.