AI Red Teaming
Definition
AI red teaming is the practice of deliberately probing an AI model with adversarial inputs to find harmful, unsafe or incorrect behaviour before the model is deployed.
Red teams act like a motivated adversary. They try to make a model produce dangerous instructions, leak private data, give confidently wrong expert advice, or bypass its safety rules. The findings are used to patch the model, add guardrails and set evaluation criteria.
Effective red teaming for a specialist model needs specialist knowledge. To test whether a medical model gives unsafe advice, the tester has to recognise unsafe advice; the same is true for legal, financial or engineering models. This is why red teaming increasingly uses verified domain experts alongside security specialists.
Red teaming is a required or recommended step under several AI governance frameworks, and results are often documented as evidence that a model was tested before release.
How Pathwize relates
Pathwize runs expert red teaming with specialists in the relevant field, and records who tested what, when, and what they found, so the testing itself is auditable.
Frequently asked
It means attacking your own model on purpose with tricky or adversarial inputs to find failures before real users do.
A mix of safety and security specialists and domain experts who can recognise subtly wrong or unsafe expert answers.
Several AI frameworks require or recommend adversarial testing and documented results before deploying higher-risk models.
Related terms