Human-in-the-loop (HIL) is a control pattern where a person reviews, edits, or approves an AI output before the system takes action. Instead of letting a model act autonomously, HIL inserts a checkpoint for judgment, policy, and accountability.
In cyber security, HIL helps reduce mistakes from hallucinations, bad data, or unsafe tool use. It is commonly used for high-impact decisions such as approving account lockouts, blocking a host, sending an alert to an incident queue, or executing a response playbook. HIL can also slow attackers when models are used in defense, because a human can catch manipulated prompts, suspicious recommendations, or unexpected actions. The tradeoff is latency and operational cost, so strong teams use HIL for risky steps while automating low-risk ones. Good HIL design pairs review with logging, role-based access, and clear approval rules.