100% Autonomous On Prem RL for AI Threat Research
We’ve been working on an autonomous threat intelligence engine for AI/LLM security. The core idea: instead of manually categorizing and severity-ranking attack signals, let an RL agent explore the threat space and figure out what’s actually dangerous through head-to-head comparisons. It uses Q-learning to decide how to evaluate each threat scenario (observe it, compare it against others, classify it, flag it, etc.) and Elo scoring to rank 91 attack signals against each other. 230K comparisons, 102K training steps, […]