Large language models (LLMs) have arrived in security in three different forms at once: as productivity tools that sit beside analysts, as components embedded inside products and workflows and as targets that attackers can probe, manipulate and steal. That convergence is why the conversation feels messy. The same capability that can summarize an incident in seconds can also generate a believable pretext for a spear phish. The same assistant that can draft detection logic can also be induced to leak sensitive context if it is wired into internal knowledge bases without guardrails. I treat LLMs as another high-impact system: define outcomes, model threats and build controls that assume the mod...