Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron

NVIDIA and CrowdStrike tested a cybersecurity workflow that makes generated detection rules prove they can recognize recorded attacks. Candidate rules are checked for valid syntax and data fields, replayed against captured activity, and returned for correction when they fail. A separate review examines whether they describe attack behavior rather than machine-specific identifiers. In the reported backtest, the proportion detecting the recorded attack rose from 16.5% with the default setup to 41.9% with the optimized pipeline. Both the models and their surrounding tools changed, so the improvement belongs to the complete system. Testing covered one scenario family in an isolated environment. System and evaluation.