LLMs perform semantic reasoning over patterns, while threat detection requires causal reasoning over system states. This architectural mismatch creates specific deployment challenges. Transformer architectures excel at pattern recognition but face structural constraints with causal inference and mechanistic understanding.

This session focuses on creating a shared understanding where LLM architecture aligns with security tasks versus where it faces fundamental limits. Learn which use cases leverage LLM strengths appropriately (threat intelligence summarization, documentation, policy interpretation) versus which require causal approaches (novel threat detection, behavioral analysis, attack chain reconstruction).

Attendees leave with practical evaluation criteria for AI security tools and implementation strategies that reduce analyst workload.