Kubernetes Namespace Isolation for Authzed.com Managed AI Projects

Question

Pulumi · Accepted Answer

When dealing with Kubernetes, namespace isolation is a method of segregating resources within the cluster. Each namespace can contain services, pods, volumes, and other Kubernetes objects, which helps in managing resource quotas, access policies, and separating environments (such as development, staging, and production) within the same cluster.

To achieve namespace isolation suitable for managed AI projects through Authzed, you would typically set up namespaces and role-based access controls (RBAC). This ensures that only authorized users and services can access the resources within a namespace. Additionally, network policies can be applied to restrict the communication between pods across different namespaces.

We'll use Pulumi to define a Kubernetes namespace and then enforce isolation by leveraging network policies and RBAC configurations. The `kubernetes.core/v1.Namespace` Pulumi resource will be used to create namespaces, and `kubernetes.networking.v1.NetworkPolicy` for network policies.

The example below demonstrates how to create a Kubernetes namespace and a basic network policy that denies ingress from other namespaces. This is a starting point for setting up isolation; you should adjust network policies and RBAC according to the specific requirements of your projects and security practices prescribed by Authzed.com.

```python
import pulumi
import pulumi_kubernetes as k8s

# Creating a new Kubernetes namespace for the managed AI projects
managed_ai_namespace = k8s.core.v1.Namespace("managed-ai-namespace",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="managed-ai-projects"
    )
)

# Applying a network policy that denies ingress from other namespaces
deny_cross_ns_policy = k8s.networking.v1.NetworkPolicy("deny-cross-ns",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="deny-cross-ns-policy",
        namespace=managed_ai_namespace.metadata.name
    ),
    spec=k8s.networking.v1.NetworkPolicySpecArgs(
        # Select all pods in the namespace
        pod_selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={}
        ),
        # Define policy types
        policy_types=["Ingress"],
        # By default, deny all ingress traffic to pods in this namespace
        ingress=[]
    )
)

# Export the namespace name
pulumi.export('namespace', managed_ai_namespace.metadata.name)
```

In the above program:

- We import the necessary Pulumi packages for Kubernetes.
- Using the `k8s.core.v1.Namespace` class, we create a new namespace named `managed-ai-projects`.
- We then create a network policy that selects all pods in the newly created namespace and sets an empty ingress policy. This means that no inbound traffic is allowed from any source outside the namespace, effectively isolating the namespace's pods.

It is important to note that this program assumes that:
- You have Pulumi installed and set up with your preferred cloud provider.
- Your Kubernetes cluster is already up and running, and you have configured Pulumi to access it by setting up the KUBECONFIG environment variable or any other means.

Once this Pulumi program is executed successfully, you will have a namespace in your Kubernetes cluster that is isolated from other namespaces with respect to network ingress. You will need to manage RBAC separately, which would typically involve creating Roles and RoleBindings to grant permissions within the namespace. Authzed.com may provide specific roles and permissions that you can implement using Pulumi's `k8s.rbac.v1.Role` and `k8s.rbac.v1.RoleBinding` resources to meet its managed service requirements.