A practical look at governing AI in threat assessment work. We cover how to run multiple models side by side and treat their disagreements as a signal to investigate, not a tie to break
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Practical AI governance for threat assessment: compare outputs across models, flag where they disagree, log every assumption, name the human who signs off, and set a review date based on your own risk tier, not a default 30 days. *(About 230 characters
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
Using several AI models to assess threats? Log where they disagree, write down the assumptions behind each call, name the person who makes the final decision, and set a follow-up review date that fits the risk
Using AI in threat assessment? Log where models disagree, write down the assumptions behind each call, name the human who makes the final decision, and set a follow-up review date that fits your risk level, not a default
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Threat assessment teams often run several AI models on the same case, and when they disagree, that gap is often the most useful signal
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