What is AI red teaming? Definition and business implications

Borrowed from cybersecurity, it is the practice of testing an AI system by simulating adverse usage: rule bypassing, sensitive-data extraction, forbidden-content generation. The aim is to identify vulnerabilities before a malicious actor exploits them in production.

Manual red teaming, where experts (cybersecurity, linguistics, law) try to derail the model. Automated red teaming, where attack models generate thousands of adversarial prompts. Bug bounty programmes, where external researchers, paid in proportion to the flaws found, test the system continuously.

For general-purpose AI models with systemic risk, the European AI Act requires documented adversarial testing (article 55), an obligation that has applied since August 2025. Since 2024, red teaming has become a documentary standard for leading laboratories, and public AI evaluation institutes (UK AI Security Institute, US Center for AI Standards and Innovation) perform it on frontier models before deployment.

Four, for stakes-bearing systems. A pre-deployment red-teaming report for each major version, with an anonymised list of corrected vulnerabilities. A minimum frequency of continuous red teaming (quarterly for critical cases, semi-annually otherwise). Transparency on the third parties involved. The right to a counter-evaluation at your expense, by a third party of your choice, on your own use cases.

No. Purely internal red teaming is insufficient. Transparency on the third parties involved (academic laboratories, public AI evaluation institutes, specialised firms) is a clause to require. These requirements are today accepted by serious market suppliers.