Decision logging is the practice of recording what a system did, when it did it, and who reviewed or approved it. In AI-assisted workflows, these records may include model inputs, outputs, confidence or uncertainty values, rejected alternatives, human overrides, and final sign-off. The goal is not just to keep an audit trail, but to make the decision process reconstructable.
In cybersecurity, decision logging matters because it supports incident response, compliance, and accountability. If an alert was blocked, escalated, or ignored, logs help explain why and reveal whether a human or system made the call. Attackers may try to tamper with or erase logs to hide malicious activity, so defenders protect them with access controls, integrity checks, and centralized storage. Good decision logging makes AI systems easier to trust, test, and challenge.