I'm working on a control-plane architecture for AI systems that enforces strict policy constraints before any execution takes place. This design aims to act as a barrier between AI or automation and their execution, ensuring robust policy enforcement that is both deterministic and fails closed. I've been testing it across various domains like fintech, health, legal, insurance, and government scenarios, and I'm eager to get your insights.
Key aspects include:
- It does NOT aim to reason like a large language model.
- Intent normalization is best-effort and can be swapped out if needed.
- Every action results in a detailed audit trail.
I'm not looking at monitoring or reporting capabilities, as the system directly blocks execution when necessary. Here are a few questions I'm grappling with:
- What assumptions might be questionable?
- Are there challenges in operating this system?
- What concerns would you have regarding deploying this in production?
2 Answers
Another day, another AI tool. Seems like everyone is throwing out these solutions without considering the real-world implications or potential pitfalls.
This is really fascinating! At my company, we've been tackling similar issues but from a different perspective. I find the deterministic aspects compelling since most approaches lean heavily on machine learning and reasoning. How do you handle policy versioning though? When you update a policy, do you keep a snapshot of the old version for audits? Also, if there's a misclassification during the intent normalization, does the whole operation just fail? That could be frustrating in production if the normalization isn't rock solid. We've seen teams hit roadblocks with false positives blocking legitimate requests, especially when they use domain-specific language that the normalizer doesn't recognize.
Great points! Yes, we do manage every decision with an immutable policy version hash, which means historical audits can always reference the exact policy that was applied at the time. Old versions are kept for review as well. Regarding normalization, we treat it as advisory rather than definitive—if we're unsure about a classification, we soft-fail by routing it to a human or a safe default instead of outright blocking. The aim is to minimize false rejections while still maintaining an auditable trail of the system's assessments.
I get that sentiment, but I'm genuinely trying to build something that adds value. I’m looking for constructive feedback here!