Secure AI Endpoints with Envoy mTLS Authentication

Question

Pulumi · Accepted Answer

To secure AI endpoints with Envoy and mutual TLS (mTLS) authentication, you'll need to create an environment where services communicate securely with one another. mTLS helps in ensuring that the traffic is both secure and trusted in both directions between a client and a server.

For this setup, you might typically have an AI service running in a Kubernetes cluster, and you would configure Envoy as a proxy to handle incoming and outgoing traffic to your service. Pulumi allows us to define this infrastructure as code, which can help you automate and reproduce your deployments with ease.

First, you'll need a Service in Kubernetes that represents your AI service. This service would need an associated Deployment that includes the containers for your AI workload as well as an Envoy proxy sidecar container that will handle mTLS.

Here's how you would define such a setup using Pulumi and Python:

```python
import pulumi
import pulumi_kubernetes as k8s

# Configuration variables for the AI service and the Envoy sidecar
ai_service_name = 'ai-service'
envoy_image = 'envoyproxy/envoy:v1.18.3'
ai_container_image = 'your-ai-container-image'
ai_container_port = 5000
envoy_container_port = 9901

# Define a Kubernetes Namespace for our service
ns = k8s.core.v1.Namespace(
    "ai-service-namespace",
    metadata={
        "name": "ai-service-ns"
    }
)

# Define the Kubernetes Deployment for the AI service and Envoy sidecar
deployment = k8s.apps.v1.Deployment(
    "ai-service-deployment",
    metadata={
        "namespace": ns.metadata["name"],
    },
    spec={
        "selector": {"matchLabels": {"app": ai_service_name}},
        "replicas": 1,
        "template": {
            "metadata": {"labels": {"app": ai_service_name}},
            "spec": {
                "containers": [
                    {
                        "name": "ai-container",
                        "image": ai_container_image,
                        "ports": [{"containerPort": ai_container_port}],
                    },
                    {
                        "name": "envoy-sidecar",
                        "image": envoy_image,
                        # Envoy configuration should be mounted through a ConfigMap and passed to Envoy using a volume.
                        "args": [
                            "envoy",
                            "-c",
                            "/etc/envoy/envoy.yaml",
                            "--service-cluster",
                            ai_service_name,
                        ],
                        "ports": [{"containerPort": envoy_container_port}],
                        "volumeMounts": [
                            {
                                "name": "envoy-config-volume",
                                "mountPath": "/etc/envoy",
                            }
                        ],
                    },
                ],
                "volumes": [
                    {
                        "name": "envoy-config-volume",
                        "configMap": {
                            "name": "envoy-config",
                        },
                    },
                ],
            },
        },
    }
)

# Define the Kubernetes Service that exposes the AI service and Envoy proxy
service = k8s.core.v1.Service(
    "ai-service",
    metadata={
        "namespace": ns.metadata["name"],
        "name": ai_service_name,
    },
    spec={
        "selector": {"app": ai_service_name},
        "ports": [
            {
                "port": ai_container_port,
                "targetPort": ai_container_port,
                "protocol": "TCP",
            },
            {
                "port": envoy_container_port,
                "targetPort": envoy_container_port,
                "protocol": "TCP",
            },
        ],
    }
)

# Output the service name and namespace
pulumi.export("ai_service_name", ai_service_name)
pulumi.export("namespace", ns.metadata["name"])
```

In this program, you define a Kubernetes Namespace, Deployment, and Service using Pulumi's Python SDK. The Deployment consists of two containers: one for your AI workload and another for the Envoy proxy. The AI container would be running your AI application, and the Envoy container runs an Envoy proxy configured for mTLS.

Please note, this example assumes that you have the necessary Envoy configuration saved in a ConfigMap named `envoy-config` which includes the necessary mTLS settings. mTLS requires setting up trusted certificates, which would be referenced in this configuration. Additionally, proper Envoy configuration is necessary to enforce mTLS, but this is beyond the scope of this example.

The important aspects of mutual TLS—such as generating certificates and keys, configuring Envoy to require and verify these certificates, and distributing the certificates securely—are not covered in this code. They would need to be addressed as part of your overall security strategy.

Also, remember that you'd need a Pulumi account, the Pulumi CLI installed, and your Kubernetes cluster configured for Pulumi to manage resources in it. Once this Python program is executed with `pulumi up`, it will create all the defined resources in your Kubernetes cluster, allowing your AI services to securely communicate with mTLS enforced by Envoy.