AlphaZero is a DeepMind reinforcement-learning system famous for mastering complex games through self-play and search rather than hand-coded rules. It matters in cyber security because it is a clear example of a high-performing model whose final decisions can be checked, while the internal reasoning may still be hard for humans to explain.
That gap is important whenever AI is used for threat detection, fraud scoring, access decisions, or automated triage. A system inspired by AlphaZero-like methods may produce strong results, but defenders still need auditability: why a rule fired, what evidence was used, and when the model might fail. In attacks, opaque AI can make malicious automation harder to inspect; in defenses, it can create trust and governance problems if operators cannot justify a decision. AlphaZero is often used to illustrate that accuracy alone is not the same as understandable or reviewable reasoning.