Keycloak for Role-Based Access Control in AI Workflows

Question

Pulumi · Accepted Answer

Keycloak is an open-source identity and access management solution that is used for modern applications and services. With Keycloak, you can add authentication to applications and secure services with minimum fuss. It allows for getting user login, registration, and provides the capacity to form roles for access control.

In a typical AI workflow, different components or services might require varying levels of access. Role-Based Access Control (RBAC) is a method of regulating access to computer or network resources based on the roles of individual users within an enterprise. Keycloak helps to manage these roles, making it a great choice for securing an AI workflow.

In the code below, we'll use Pulumi with the Keycloak provider to create some of the basic configurations typically required for setting up Keycloak with role-based access control. These will include:

1. A Keycloak Realm: This is a top-level container in Keycloak, which represents a space where a set of users, credentials, roles, and groups reside.
2. A Keycloak Client: Clients in Keycloak represent applications and services that are allowed to authenticate users.
3. Keycloak Roles: We define a set of roles that will be used to control access levels.
4. Keycloak Users: We will create some users and assign roles to them, representing how you might have different AI workflow components or team members.
5. Keycloak Group: Groups allow you to group users and manage their roles and attributes together. This is useful if you want all members of a certain group to have the same access rights.

Below is the Pulumi program written in Python to create these resources:

```python
import pulumi
import pulumi_keycloak as keycloak

# Create a new Keycloak Realm to which all other entities will belong.
realm = keycloak.Realm("myRealm",
    realm="example",
    enabled=True,
    # Further realm configurations can be set here
)

# Define an OpenID client that will be used to authenticate users in this realm.
client = keycloak.openid.Client("myClient",
    realm_id=realm.id,
    client_id="example-client",
    name="Example Client",
    enabled=True,
    access_type="CONFIDENTIAL",
    valid_redirect_uris=["http://localhost:3000/*"],
    # Further client configurations can be set here
)

# Create a role that will define a user's access level.
user_role = keycloak.Role("userRole",
    realm_id=realm.id,
    name="example-user",
    # Additional role properties can be set here.
)

# Create a user and assign the previously created role.
user = keycloak.User("exampleUser",
    realm_id=realm.id,
    username="example-user",
    enabled=True,
    email_verified=True,
    # User attributes can be further customized here.
)

# Assign the created user role to the user.
user_roles = keycloak.UserRoles("exampleUserRoles",
    realm_id=realm.id,
    user_id=user.id,
    role_ids=[user_role.id],
)

# Define a group for AI Workflow users.
group = keycloak.Group("aiWorkflowGroup",
    realm_id=realm.id,
    name="AI Workflow Users",
    # Additional group properties can be set here.
)

# Assign roles to the group. All group members inherit these roles.
group_roles = keycloak.GroupRoles("aiWorkflowGroupRoles",
    realm_id=realm.id,
    group_id=group.id,
    role_ids=[user_role.id],
)

# Export some of the Keycloak configuration properties.
pulumi.export("keycloak_realm", realm.realm)
pulumi.export("keycloak_client_id", client.client_id)
```

In this program:
- We start by creating a realm. This is the space where your users and roles will be managed. By enabling it, we make it active and ready for further configuration.
- We set up a client. In the context of Keycloak, a client can be thought of as a service that users can log into, such as your AI application or workflow. The `access_type` is set to "CONFIDENTIAL" which means the client needs to provide a secret to initiate the login process.
- We define a role, which can later be assigned to users or groups to control their access levels. In real-world usage, you might have roles like "analyst" or "engineer" that have different access levels in an AI workflow.
- We then create a user and make sure they are enabled and have their email verified. This would represent an individual using the AI services.
- The `UserRoles` resource assigns the role to the user. This step is crucial in RBAC as it actually grants the access level as defined by the role.
- We create a group and assign roles to it. Groups are helpful in managing roles and attributes for a collection of users. When a user is added to a group, they automatically gain the roles associated with that group.

This basic setup forms the backbone of using Keycloak to manage RBAC in your AI workflows. More complex workflows can involve additional configuration of clients, realms, groups, and roles as well as setup of custom protocol mappers, scopes, and client scopes for fine-grained access control.