Role-Based Access for Distributed AI Applications

Question

Pulumi · Accepted Answer

To implement role-based access control (RBAC) for distributed AI applications, especially if they are running on a Kubernetes cluster, you can use Kubernetes RBAC resources like `Role`, `RoleBinding`, `ClusterRole`, and `ClusterRoleBinding`. These resources help you define roles with specific permissions and assign these roles to users or groups of users.

Below is a high-level overview of how these resources play a part in the role-based access control system:

- **Role**: Defines permissions within a particular namespace. Use this to grant permissions locally to the namespace where the AI application resides.
- **RoleBinding**: Grants the permissions defined in a Role to a user or set of users within the same namespace as the Role.
- **ClusterRole**: Like a Role, but for the entire cluster. Use this for permissions that are applicable across the entire cluster, not just a single namespace.
- **ClusterRoleBinding**: Grants the permissions defined in a ClusterRole to a user or set of users cluster-wide.

For instance, if you have an AI application that requires access to Kubernetes resources like pods, services, and config maps in a specific namespace, you would define a `Role` with the necessary permissions. Then, you would create a `RoleBinding` to attach that role to the service account used by your AI application.

Let’s write a Python program using Pulumi to set up these resources. In this example, we’ll create a `Role` that permits read-only access to pods in a namespace and bind this role to a specific user represented by a service account.

```python
import pulumi
import pulumi_kubernetes as kubernetes

# Create a Kubernetes Role that allows read-only access to pods in the 'ai-applications' namespace.
pod_reader_role = kubernetes.rbac.v1.Role(
    "pod-reader-role",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        namespace="ai-applications"  # Assuming 'ai-applications' is the namespace for the AI applications.
    ),
    rules=[kubernetes.rbac.v1.PolicyRuleArgs(
        api_groups=[""],  # The empty string "" indicates the core API group.
        resources=["pods"],
        verbs=["get", "list", "watch"],
    )]
)

# Create a ServiceAccount that will be used by the AI application.
ai_service_account = kubernetes.core.v1.ServiceAccount(
    "ai-service-account",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        namespace="ai-applications"  # The same namespace as the Role.
    )
)

# Bind the 'pod-reader-role' Role to the 'ai-service-account' ServiceAccount in the 'ai-applications' namespace.
role_binding = kubernetes.rbac.v1.RoleBinding(
    "pod-reader-role-binding",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        namespace="ai-applications"  # Match the namespace of the Role and ServiceAccount.
    ),
    role_ref=kubernetes.rbac.v1.RoleRefArgs(
        api_group="rbac.authorization.k8s.io",  # Use the RBAC API group.
        kind="Role",  # Binding a 'Role' as opposed to a 'ClusterRole'.
        name=pod_reader_role.metadata.name,  # Reference the created Role by name.
    ),
    subjects=[kubernetes.rbac.v1.SubjectArgs(
        kind="ServiceAccount",
        name=ai_service_account.metadata.name,  # Reference the ServiceAccount by name.
        namespace="ai-applications"
    )]
)

# Export the ServiceAccount name that should be used in the AI application's deployment.
pulumi.export("service_account_name", ai_service_account.metadata.name)
```

This Pulumi program does the following:

1. Defines a `Role` called `pod-reader-role` in the `ai-applications` namespace that allows performing "get", "list", and "watch" operations on pods.
2. Creates a `ServiceAccount` named `ai-service-account` which will be used by the AI application to interact with the Kubernetes API.
3. Establishes a `RoleBinding` called `pod-reader-role-binding` to give the previously defined `Role` permissions to the `ServiceAccount`.

You should deploy this configuration to your Kubernetes cluster using the Pulumi CLI. Ensure you have the correct Kubernetes context set up for the `ai-applications` namespace which contains your distributed AI applications. Once applied, the AI application should be configured to use the `ai-service-account` ServiceAccount for its Kubernetes API interactions, thereby enforcing the defined RBAC rules.