Fine-Grained Policy Enforcement for LLMs using Ory Oathkeeper

Question

Pulumi · Accepted Answer

To enforce fine-grained policies for Large Language Models (LLMs) using Ory Oathkeeper, you would implement a gateway that intercepts incoming requests to the LLM service and evaluates them against a set of predefined rules. Ory Oathkeeper is a cloud-native identity and access proxy that can make access control decisions based on who is making the request, the request itself, and other context.

Here's a high-level overview of what you need for fine-grained policy enforcement:

1. **Ory Oathkeeper**: Acts as a gatekeeper for your LLM service.
2. **Access Rules**: Define who can do what under which circumstances. These rules would be written in a format that Ory Oathkeeper understands.
3. **LLM Service**: The actual service that provides access to Large Language Models. The LLM service must be able to be proxied by Oathkeeper; in the Pulumi context, you could host this service on cloud infrastructure like AWS, GCP, or Azure.

For illustrative purposes, below is a Pulumi program that sets up an example LLM service (this is a mock service, as Pulumi does not directly interact with Ory Oathkeeper, but it may be hosted on any cloud service as an endpoint) and Ory Oathkeeper proxy on AWS using an `awsx.ecs.FargateService`. This service would be the one protected by access rules.

Keep in mind that this is a simplified example to give you an idea of using Pulumi to set up cloud infrastructure. In reality, you would also need to set up Ory Oathkeeper, which would involve writing the configuration for Ory Oathkeeper to define your access rules separately.

```python
import pulumi
import pulumi_awsx as awsx

# Create a VPC for our service.
vpc = awsx.ec2.Vpc("llm-vpc", number_of_availability_zones=2)

# A security group to allow HTTP access.
sg = awsx.ec2.SecurityGroup("llm-sg", vpc=vpc)

# Allow incoming HTTP requests.
sg.allow_inbound("http-access", type="ingress", from_port=80, to_port=80, protocol="tcp", cidr_blocks=["0.0.0.0/0"])

# Define the container image for the LLM service.
image = awsx.ecs.Image.from_path("llm-service", "./app")

# Define the service using AWS Fargate for serverless container execution.
llm_service = awsx.ecs.FargateService("llm-service",
    vpc=vpc,
    desired_count=1,
    task_definition_args=awsx.ecs.FargateServiceTaskDefinitionArgs(
        containers={
            "llm-service": awsx.ecs.TaskDefinitionContainerDefinitionArgs(
                image=image,
                memory=512,
                port_mappings=[awsx.ecs.PortMappingArgs(
                    container_port=80,
                    target_group=awsx.lb.TargetGroupArgs(
                        vpc=vpc,
                        port=80,
                        protocol="HTTP",
                        health_check=awsx.lb.TargetGroupHealthCheckArgs(
                            path="/healthz",
                            interval_seconds=10,
                            timeout_seconds=5,
                            healthy_threshold=2,
                            unhealthy_threshold=5,
                            protocol="HTTP"
                        ),
                    ),
                )],
                cpu=256,
            ),
        },
    ),
    security_groups=[sg.id],
    subnets=vpc.private_subnet_ids,
)

# Export the URL so we can easily access our service.
pulumi.export("llm_service_url", llm_service.load_balancer.hostname)
```

This program sets up a VPC, security group, container image, and a Fargate service to host our mock LLM service. We export the URL of the load balancer so we can access the service over the web.

To secure the LLM service with Ory Oathkeeper, you would typically set up Oathkeeper as a reverse proxy in front of the LLM service to handle requests and enforce access control decisions. However, integrating Ory Oathkeeper into your infrastructure is out of the scope of this example and Pulumi's cloud infrastructure capabilities. You'd have to manually configure Ory Oathkeeper to enforce policies for incoming requests to the LLM service based on your own access rules.