AI standards are technical rules, test methods, and shared practices used to measure and compare AI systems. They define what to test, how to score results, and which safety or performance thresholds matter. In practice, standards help organizations evaluate accuracy, robustness, bias, and resistance to misuse in a consistent way.
In cyber security, AI standards matter because attackers can exploit weak models, poor validation, or inconsistent testing. A system that has not been benchmarked against adversarial inputs may fail under prompt injection, data poisoning, or model evasion. Standards also support defense: they let teams compare models fairly, document risk, and repeat tests after updates. For governments, vendors, and security teams, AI standards make AI governance more reliable and help turn vague trust claims into measurable controls.