An evaluation loop is a repeatable feedback process that measures how a system behaves, then uses human review or operational signals to improve it. In AI systems, that can mean checking outputs against labels, policies, logs, or analyst judgments, and then adjusting prompts, rules, or model weights based on the results.
In cyber security, evaluation loops matter because security tools must stay accurate as attackers change tactics. A phishing filter, malware classifier, or SOC assistant can drift, miss new patterns, or produce unsafe recommendations if it is never tested against real-world traffic. Defense teams use evaluation loops to sample alerts, review false positives and false negatives, and verify that changes reduce risk without breaking workflows. Attackers can also exploit weak evaluation loops by feeding systems misleading data, causing bad tuning or blind spots. A strong loop makes security AI measurable, auditable, and harder to manipulate.