Reconstruct the Near-Miss Before It Becomes a Finding
Explore how to reconstruct AI-generated near-misses by documenting inputs, outputs, approvals, and limitations. This lesson helps you understand the importance of building test plans, monitoring, and record-keeping to ensure AI decisions are explainable and defensible, reducing risk and ensuring accountability in sensitive environments.
A fluent AI-written incident summary can look finished even when one line is wrong. The mistake is not usually a spelling error or a broken date. It is a confident claim that shifts priority, narrows scope, or assigns cause, and it slips through because the writing reads like a practiced analyst wrote it. That failure is rarely fixed by saying people should read more carefully. It is fixed by being able to show, later, what was checked and why anyone relied on it.
A reviewer almost pastes a draft summary into an official report. The draft says a mitigation is confirmed, so the event can be downgraded. The reviewer checks the cited advisory and finds the wording does not support that conclusion. The summary gets corrected before it becomes part of the record, but the near-miss leaves a harder question. If the wrong sentence had made it through, could the team reconstruct what went in, what came out, ...