Picture the moment after an AI issue is reported.
A security analyst is reviewing a ticket reporting that an internal AI tool produced the wrong recommendation in a live business workflow. The risk is not theoretical anymore. Someone wants to know whether this is a security incident, a model issue, a privacy issue, a vendor issue or just “something the AI did.” The risk register has a line item for inaccurate output, and it may even have a severity rating.
What it does not have is an answer to the question everyone is now asking: who has the authority to stop this thing?
That is the gap many AI governance programs still need to close. Organizations are getting better at identifying AI risks,...