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A practical guide to governing AI tools used in threat assessment. Learn how teams handle it when two models disagree on a risk rating, why each working assumption needs to be written down before a decision is made, and how to name who owns the final call
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How threat assessment teams can use AI with a clear paper trail: compare models when they disagree, write down the assumptions, name the human who makes the call, and set a review date for each case
Threat assessments often rest on judgment calls nobody writes down. This guide shows how to put AI governance around that work. Run more than one model on the same case, and log where they disagree and why
When AI models disagree on a threat assessment, we log where and why. Each call records its assumptions, names the person who owns the final decision, and sets a follow-up review date based on the risk, not a fixed 30 days
AI can flag threats faster, but it shouldn't decide alone. Learn how to compare outputs across multiple models, record the assumptions behind each call, name the person accountable for the outcome, and set a follow-up review that fits your risk.
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A practical look at governing AI in threat assessment work. Teams running more than one model can see where those models disagree, and that disagreement becomes a signal to investigate rather than a result to average away