The SOC AI Architecture
AI sits at multiple layers in a modern SOC — classical ML for detection, GenAI for narrative and reasoning
Two Layers of AI
Modern SOC tools have two AI layers: classical ML for detection (anomaly, UEBA, malware classifiers, BEC) and GenAI on top for narrative, summarization, and reasoning. The base detection is still classical ML; the GenAI sits above translating signal into analyst-friendly language.
Hype Watch
AI-powered detection sometimes means we have a few ML models for one use case. When you evaluate, ask: which detections are AI-driven? What is the false positive rate vs. the previous rules engine? Can you show me a model card or evaluation methodology?
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