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AI governance for threat assessment, without the buzzwords. When multiple models disagree on a risk signal, the disagreement gets logged, not averaged away. Each assumption behind a score is written down where reviewers can challenge it
AI governance for threat assessment starts with a simple question: who owns the call? This guide explains how to run several AI models on the same case and treat their disagreements as a signal to investigate, not a tie to break
Threat assessment teams using AI need a paper trail. Learn how to compare outputs across multiple models, log where they disagree, write down assumptions before decisions, name the person who signs off, and set a follow-up review that fits the case
Threat assessment teams using AI models get a plain record of where the models disagree, which assumptions drove each call, and the named person who made the final decision. Follow-up reviews are set case by case