Explainable AI (XAI) is AI designed to make its decisions easier for people to understand and evaluate. Instead of returning only a score or label, an XAI system shows the main signals, rules, or evidence behind its output.
In cyber security, XAI matters because security teams must decide whether to trust an alert, block an action, or escalate an incident. Explanations help analysts validate detections, tune thresholds, find false positives, and spot bias or weak inputs. In defense, XAI is used in SIEM triage, malware classification, fraud detection, and identity-risk scoring. In attacks, opaque models can hide poisoned data, manipulated inputs, or brittle decision rules, making abuse harder to notice. XAI does not remove risk, but it makes automated decisions reviewable, auditable, and easier to use safely.