AI Incident Reporting Starts Before the Incident

AI incident reporting requirements are increasingly time-sensitive, with the EU AI Act setting deadlines as short as two days for certain serious incidents and other regimes imposing rapid notification duties on frontier model developers. The passage argues that organizations’ ability to meet these deadlines depends less on post-incident investigation than on evidence created before agents enter production, including independent verification, tamper-resistant and human-readable action records, clear permission boundaries, tested shutdown procedures, and defined notification responsibilities. It distinguishes detection, which security platforms are increasingly incorporating through agent observability tools, from reconstruction, which requires deployers to control and access evidence without relying on the implicated model or vendor. A proposed 48-hour disclosure test assesses whether an organization can answer six core questions using pre-existing evidence, while a maturity model describes progress from anecdotal incident handling to continuously tested and shared lessons. The passage recommends release gates, independent verification, rehearsed reporting runbooks, and contractual evidence obligations for vendors, and presents TestMu AI’s testing products as tools intended to generate deterministic pre-production evidence and convert incidents into future regression tests.

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