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Plain-spoken guide to AI governance in threat assessment: log where 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 case

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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

Running more than one AI model on a threat assessment often shows where they disagree, and that disagreement is worth recording

AI governance for threat assessment means keeping a clear record of how decisions get made. When multiple models disagree on a risk signal, that disagreement gets logged, not smoothed over

Using AI in threat assessment? Keep the process defensible. Compare outputs from several models and treat disagreement as a signal to investigate, not a number to average. Write down every assumption behind a score so reviewers can challenge it later

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Threat assessment teams using AI need a paper trail. This guide covers logging where models disagree, recording the assumptions behind each call, naming the person who signs off, and setting a dated follow-up review