LLMs can write Sigma rules, translate between SIEM platforms, and review detection logic. But they cannot replace the adversarial mindset that makes detection engineering effective.
Oluwatimilehin Ademosu · Apr 1, 2026 · 10 min read
Large language models are changing how we write, test, and maintain detection rules. But the fundamentals of detection engineering remain unchanged: you still need to understand attacker behavior, your environment's baseline, and the data sources that matter.
What changes is the speed at which we can iterate. What stays the same is the need for human judgment about what to detect and why.
The highest-value application of LLMs in detection engineering is not writing Sigma rules from natural language prompts. It is analyzing detection coverage gaps and suggesting improvements based on threat intelligence.
LLMs cannot replace the adversarial mindset that makes detection engineering effective. They can generate syntactically correct rules, but they cannot reason about whether a detection will produce actionable alerts in your specific environment.
The gap between a rule that compiles and a rule that catches attackers without drowning analysts in noise is where human expertise remains irreplaceable.
Start by using LLMs as a code review tool for your existing detection library. Feed your rules through a model trained on detection engineering best practices and fix the issues it identifies. This gives you immediate value without the risk of deploying AI-generated detections directly.
Detection engineering is becoming more accessible, not less important. LLMs lower the barrier to writing initial rules, but they raise the bar for what constitutes excellent detection engineering. The best practitioners will be those who use AI as leverage while maintaining deep expertise in attacker tradecraft.
Written by
Oluwatimilehin AdemosuTimmy writes about detection engineering, automation, and the tools shaping the next generation of security operations centers.