Report Near-Misses Using Local Policy, Not Assumptions
Understand how to handle AI near-misses by following your organization's local policies rather than assumptions. This lesson guides you to preserve essential evidence, contain the spread of errors, report incidents accurately, and update AI decision controls. You will learn to create defensible reporting that supports ongoing risk management and accountability in AI use.
A team can do everything the packet asked for and still get a close call. The AI-written summary reads clean, so it slips into an internal tracker as if it were verified. Someone catches the error before it leaves the team, but the workflow has already been nudged by it. That is the moment that decides whether the team has a defensible process or just a well-formatted decision packet.
The common mistake is treating a near miss like an embarrassment to bury, or like a full incident to declare, without checking what local policy actually says. That mistake comes from speed and uncertainty. The work feels like common sense triage, but the outcome turns on what evidence is preserved and who is told, because later review depends on what can be reconstructed.
The close call starts with an AI-assisted draft that was used as a decision artifact. An analyst pasted a summary into an internal prioritization queue, then a second analyst compared it to the public source advisory the summary was drafted from and found a material mismatch. The input was in scope; the failure was treating the draft as verified without the required review. The team corrected the queue entry, but the original text and timestamps still exist in the system’s history. The team now has to decide what to freeze, what to clean up, and what to report through the ...