Most teams are still here: "Make the model better." But production systems require: "Make the system safer."
LLMs should not be trusted to execute. They should be used to propose. Systems must:
- →validate
- →constrain
- →enforce
That's the difference between: AI as a feature vs AI as infrastructure.
If your current pipeline looks like: LLM → Execution You are not running an AI system. You are running unverified automation.
And that doesn't fail immediately. It fails gradually.
Map your current workflow:
LLM output → ? → execution
If that "?" is not a clearly defined validation layer, you're operating on assumptions.
Enterprise AI|LLM Controls|AI Strategy|AI Architecture