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Practical AI governance for threat assessment: compare outputs when models disagree, log the assumptions behind each call, name the person who owns the final decision, and set a follow-up review date that fits the case

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Run several AI models on the same threat assessment and log where they disagree. Every assumption gets written down, a named person owns the final call, and each case gets a follow-up review date based on its own risk level

Practical guidance for AI in threat assessment: flag when models disagree, record the assumptions behind each call, name the human who makes the final decision, and set a follow-up review date that fits the risk level

**Full blurb (89 words):** Threat assessment teams are starting to use AI models to help sort reports, but the output alone is not a decision

A practical look at governing AI in threat assessment work. When several models disagree on a risk rating, the disagreement gets logged rather than averaged away. Every assumption behind a score is written down where a reviewer can challenge it

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