Insider threat detection faces challenges due to failed data science projects and unreliable machine learning models. A pessimistic approach suggests that ML should only be used for threats explicitly modeled. Threat modeling can reveal simpler, more effective detection strategies without relying on complex ML systems, improving project success rates by focusing on static detections and multi-dimensional correlations rather than attempting to profile anomalous user behavior. This pragmatic method allows security teams to make incremental progress in reducing insider risks through structured threat analysis and detection engineering.
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