A proactive testing approach where internal or external experts simulate attacks or misuse scenarios to uncover vulnerabilities in AI systems.
A structured adversarial exercise in which specialized teams (“red teams”) attempt to defeat model safeguards - via prompt injections, data poisoning, API abuse, or model extraction. Red teaming identifies gaps in defenses, informs mitigation strategies, and validates that security and policy layers hold up under realistic threat simulations. Governance dictates red-team scope, rules of engagement, and required remediation reporting.
A financial-AI platform hires an external red team to attempt data-extraction attacks on its API. The team successfully reconstructs sample training data. Based on the findings, the platform adds rate limiting, differential-privacy noise, and stronger authentication - addressing critical vulnerabilities before public launch.
