AI Risk Register vs. Incident Plan: Key Differences

A spreadsheet of AI risks will not tell your team what to do when an AI model breaks. While organizations are getting better at identifying and documenting AI risks, they often lack an executable response model for when these risks become real events. Here's the thing: a risk register can document concerns, but it cannot preserve evidence, notify leadership, assess impact, or decide if an AI system should keep running. For example, if an internal AI tool produces a wrong recommendation, the risk register might note "inaccurate output." However, it won't answer who has the authority to stop the system. What's interesting is that this distinction matters in security programs. A list of vulnerabilities is not a vulnerability management program. Similarly, an AI risk register, while useful for visibility, is not a control. An entry like "model output may be inaccurate" doesn't define who monitors quality, what error level is acceptable, or who can pause the system. The bottom line: having policies and risk registers doesn't automatically create operational readiness. When an AI incident occurs, the real test is whether an organization knows what to do next.