Human-AI oversight is a workflow design in which people keep review and decision authority over AI advice. The model can suggest, draft, summarize, or flag risk, but a human must validate the output before it is acted on. In security work, this separation matters because an AI system can be fluent, confident, and still wrong. If users treat the model’s response as approval instead of guidance, mistakes can move forward with less scrutiny than they need.
In cyber security, oversight helps prevent automation bias, sycophancy, and other trust failures from becoming operational weaknesses. It appears in defenses as mandatory review for policy changes, escalation paths, evidence checks, and high-impact approvals. In attacks, adversaries may try to exploit overtrust by feeding misleading prompts or biased context so the assistant reinforces a bad decision. Strong oversight limits that risk by requiring independent verification, clear ownership, and the ability for people to override the model when the stakes are high.