Productive override is a human challenge to an AI recommendation that improves the final decision instead of simply copying or rejecting the model. The reviewer uses independent judgment, adds context the model missed, and reaches a better outcome. This is different from blind trust or reflexive disagreement: a productive override is justified, traceable, and useful.
In cyber security, productive override matters in alert triage, phishing review, fraud detection, and access approvals. LLMs and other AI systems can produce confident but incomplete recommendations, especially when a polished explanation makes the answer feel safer than it is. A productive override can stop a false negative, catch a suspicious login the model downplayed, or prevent an unsafe block on legitimate activity. Defenders support it with second-pass review, override logging, and policies that require humans to explain when they disagree with automation. The goal is not to ignore AI, but to keep human skepticism active where mistakes are costly.