Output controls are the review and approval rules that determine how AI-generated results are checked before they are used. They define who can rely on the output, what must be verified by a human, and when an automated result is too risky to accept as-is. In practice, output controls may include validation steps, sign-off requirements, confidence thresholds, and logging of who approved the result.
They matter in cyber security because AI outputs can be wrong, biased, confidential, or manipulated. If an attacker influences the model through prompt injection, poisoned data, or unsafe inputs, the output may contain harmful instructions, leaked information, or flawed decisions. Strong output controls reduce that risk by forcing review before release, blocking sensitive content, and limiting downstream automation. In contracts and security policies, they help define accountability: the model may generate the result, but a person or system must decide whether it is safe to use.