# Building a Production-Ready AI Governance Stack (Part 3/3)

The architecture you've described is a robust and secure approach to implementing governance controls for AI systems, particularly in highly regulated industries. Let's break down the key components and their roles:

Layer 1: Request Handling

This layer captures all requests made to the AI system, ensuring that every interaction with the model is recorded. This includes metadata such as timestamps, user IDs, request hashes, and any other relevant information needed for auditing purposes.

python
 
1class RequestHandler:
2    async def handle_request(self, request):
3        # Log request details
4        log_entry = {
5            "timestamp": utcnow(),
6            "user_id": request.user_id,
7            "request_hash": hash(request.data),
8            "model_name": request.model_name,
9            "parameters": request.parameters
10        }
11        
12        # Store in immutable storage
13        await s3_client.put_object(
14            Bucket=LOG_BUCKET,
15            Key=f"requests/{log_entry['timestamp']}.json",
16            Body=json.dumps(log_entry),
17            ObjectLockMode="COMPLIANCE"
18        )

Layer 2: Policy Enforcement

This layer enforces the policies defined by your organization. It checks if a request complies with the rules set forth in your governance framework

Read the full article at DEV Community

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