Last reviewed: May 24, 2026

What is AI red teaming? Definition and business implications

Red teaming, borrowed from cybersecurity, is the practice of testing an AI system by simulating adverse usage attempts: rule bypassing, sensitive-data extraction, forbidden-content generation. It aims to identify vulnerabilities before a malicious actor exploits them in production.

AI red teaming combines three approaches. Manual red teaming: experts (often from cybersecurity, linguistics, law backgrounds) try to derail the model with elaborate prompts, role-plays, and guardrail bypasses. Automated red teaming: attack models generate thousands of adversarial prompts to explore the vulnerability space. Bug bounty programmes: external researchers, paid in proportion to flaws found, test the system continuously. Since 2024, red teaming has become a documentary standard for leading laboratories. Anthropic publishes its Responsible Scaling Policy (AI Safety Levels) with red-teaming requirements by capability level. AI Safety Institutes (UK AISI, US CAISI) perform external red teaming on frontier models before deployment. The European AI Act mandates red teaming for high-risk systems.

Concrete example

Anthropic documented in 2025 its red-teaming protocol for Claude 3.7 Sonnet, tested jointly with the UK AI Security Institute and the US Center for AI Standards and Innovation (CAISI). The work focused on risks of biological, chemical, or nuclear misuse. The government agencies identified several previously unknown attack vectors, some of which led Anthropic to modify its real-time classifiers before going to production. The transparency of the protocol, published in the ASL-3 Deployment Safeguards Report in May 2025, has become an industry reference.

To require contractually

Four clauses to require in any contract with an AI vendor for stakes-bearing systems (legal, financial, medical, HR). First, the obligation to provide a pre-deployment red-teaming report for each major version of the model, with an anonymised list of identified and corrected vulnerabilities. Second, the minimum frequency of continuous red teaming during the contract: quarterly for critical cases, semi-annually for others. Third, transparency on the third parties involved (academic laboratories, AI Safety Institutes, specialised firms): purely internal red teaming is insufficient. Fourth, the right to a counter-evaluation at your expense, by a third party of your choice, on your specific use cases. These clauses are not bespoke high-end add-ons: they are today accepted by serious market suppliers.

Sources

Strengthening our safeguards through collaboration with US CAISI and UK AISI, Anthropic, September 2025. https://www.anthropic.com/news/strengthening-our-safeguards-through-collaboration-with-us-caisi-and-uk-aisi (accessed 2026-05-24)
AI Safety Level 3 Deployment Safeguards Report, Anthropic, May 2025. https://www.anthropic.com/asl3-deployment-safeguards (accessed 2026-05-24)

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What is AI red teaming? Definition and business implications

Concrete example

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Further reading

Sources