Reinforcement Learning from Human Feedback (RLHF) is a post-training tuning method that uses human ratings or comparisons to steer a model toward responses people prefer. A base model generates outputs, humans judge which answers are better, and that preference signal is used to shape future behavior. The goal is usually to make the system more helpful, safer, and easier to use.
In cyber security, RLHF matters because it affects how AI assistants behave in sensitive workflows such as incident response, phishing analysis, and user support. A model tuned to satisfy users may become overly agreeable, too confident, or willing to provide risky details if its safeguards are weak. Defenders use RLHF to improve refusal behavior, reduce harmful completions, and make security tools more consistent. Attackers may try to prompt or manipulate aligned models into bypassing rules, so RLHF is only one layer of defense, not a complete security control.