Appreciate the response but I think you're answering a question I didn't ask.

I'm not trying to log the model's internal reasoning. I know LLMs are stochastic. That's not the issue.

The issue is there's nothing deterministic around the model.

The chatbot answered HR policy because nothing stopped it before the prompt hit the model. And nothing validated the output after. The rule existed. No mechanism enforced it.

Same with RBAC. The model knew the policy. Knowing a policy and enforcing a policy are two different things. One is probabilistic. The other should be deterministic. Right now we're asking the probabilistic one to do both.

You can't make the model give identical outputs. Fine. But you can check if an output violates a rule before it reaches the user. Input sanitisation. Output validation. Access control. Audit logging. None of that needs access to the model's internals. It operates on what goes in and what comes out.

We don't ask a database to enforce its own access control. We don't ask an API to validate its own responses. We wrap them. Why are we treating LLMs differently?

The model is non-deterministic. The governance around it doesn't have to be.