Tiered Architecture Beats LLM-as-Judge for AI Security, Developer Argues

A developer building an AI security tool argues that using a large language model to evaluate every incoming request is slow, costly, and itself vulnerable to jailbreaking. Instead, they propose a four-tier detection system that starts with cheap, fast methods like regex and classical machine learning before escalating to heavier models only when necessary. The first two tiers are deterministic and handle the vast majority of traffic at near-zero cost, while an optional transformer layer and a user-supplied LLM handle genuinely ambiguous cases. This approach means LLM-level inference costs are incurred only on a small fraction of requests rather than across all traffic. Because the always-on tiers run locally, the entire system can operate air-gapped inside a private network without contacting any external hosted model.