Teaching AI to Reason Through Detection Triage

CrowdStrike describes research on a reasoning-enabled AI system for cybersecurity detection triage that classifies endpoint alerts as true or false positives while producing an auditable explanation of its assessment. Built on the NVIDIA Nemotron 3 Nano 30B-A3B model and trained using prompt optimization, self-training, reinforcement learning, and separate confidence calibration, the system evaluates contextual evidence such as process behavior and parent-child relationships rather than issuing a direct label alone. On a held-out set of Windows endpoint detections, CrowdStrike reports 82.6% overall accuracy and substantial gains at high-confidence thresholds, including a 43-point increase in false-positive recall and an 18.3-point increase in true-positive recall compared with a direct-label approach. The findings suggest that specialized training on real security data can outperform larger general-purpose models for triage, potentially enabling more benign alerts to be safely closed automatically and more genuine threats to be prioritized for analysts.

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