Explainable AI, or XAI, refers to AI systems that can show the evidence, logic, or features behind a decision in a way humans can inspect. Instead of returning only a score, recommendation, or label, the system explains why it reached that result. In cybersecurity, that transparency is critical because defenders need to trust and verify AI-assisted conclusions before acting on them.
XAI matters in tasks such as threat detection, vulnerability prioritization, and phishing analysis. A model may flag an asset as high risk or a message as malicious, but analysts still need to know which signals drove the output, such as exposure, behavior, or policy context. Attackers can also exploit opaque models by hiding in data gaps or confusing automated decisions. Explainable AI helps security teams audit outcomes, spot errors, and use AI as decision support rather than a black box.