AI Red Teaming

AI red teaming is structured adversarial testing of a model or AI application: probing it with jailbreaks, prompt injections, data-extraction attempts, and harmful requests to surface weaknesses a normal test suite would miss. It borrows the security discipline of red teaming and applies it to the open-ended failure modes of generative systems.

Red teaming pairs naturally with policy: findings become rules, and a candidate policy can be pressure-tested against adversarial traffic before it goes live. Synthetic adversarial data makes this repeatable at scale, so a team can see exactly what a policy would block, redact, or escalate before a single real request is affected.