Situations
Reassuring leadership on the risk of an AI project
The question is not whether the model can be wrong, it can. It is what happens when it is wrong, and who will have seen it before anything leaves the building.
What we observe
- The project is stuck in committee with no specific risk named
- Nobody can say which actions the agent would be allowed to take on its own
- The GDPR question arrives late, once the architecture is chosen
- No recovery scenario has been written for the day the system gets it wrong
What usually blocks
Leadership does not block on uncertainty, it blocks on the absence of a perimeter. As long as nobody has written down what the system may do alone, perceived risk stays infinite.
The useful work is sorting actions into three buckets: those that go out automatically, those that need a confirmation, and those that are simply forbidden. That table fits on one page and unblocks more committees than a technical demo.
What we do
- 01We write the action policy: automatic, confirm, forbidden
- 02We put human validation where an error is expensive, not everywhere
- 03We keep a trace of every decision, with who approved what and when
- 04We set the kill thresholds, so everyone knows when we switch it off
The proof
Deep case with zero automatic publishing: every output goes through a validation queue.
The method in full
The reference guide on the subject, with its definition, its sources and its guardrails.
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