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When AI models disagree on a threat case, who makes the call? This piece covers a governance setup that runs several models on the same case, records where they diverge, and writes down the assumptions behind each output
Practical AI governance for threat assessment teams. When models disagree, log the reasoning behind each call, name the human who makes the final decision, and schedule a follow-up review sized to the case risk, not a fixed 30-day default
Using AI in threat assessment raises a simple question: who answers for the call? This guide walks through a governance setup that runs several AI models on the same case and records where they disagree, instead of trusting one output
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A practical look at governing AI in threat assessment work
A plain-language guide to governing AI in threat assessment. When two models disagree on a risk rating, the disagreement gets logged, not averaged away. Each assumption behind a score is written down so a reviewer can challenge it later
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When AI models disagree on a threat assessment, that gap is the signal to look at. We record each model's assumptions, name the person accountable for the decision, and set a follow-up review date that fits the case, not a default
When AI models disagree on a threat assessment, the disagreement gets logged. Each review records its assumptions, names the human who owns the final call, and schedules a follow-up check. The review interval is set per case, not fixed at 30 days