Human on the loop is an AI supervision model in which a person does not perform every task manually, but watches the system’s behavior and can step in when something looks wrong. It is common in security-sensitive AI workflows where models can recommend actions, generate code, route tickets, or call tools through APIs.
This matters because AI systems can be fast but imperfect. A human supervisor can approve high-risk actions, stop unsafe outputs, and catch errors caused by prompt injection, bad data, policy violations, or model drift. In defense, this usually means logging, alerts, review queues, and a clear override or rollback path. In attacks, adversaries try to make the human trust a misleading result, so effective supervision must be explicit, scoped, and easy to interrupt. Human on the loop is a practical control that helps teams use AI while keeping accountability with people.