Red teaming is a structured testing process in which a group of authorized experts intentionally imitates adversarial attacks or manipulations to identify weaknesses in an artificial intelligence (AI) system. The goal is to strengthen the system’s security, fairness, and reliability by exposing flaws before they can be exploited in the real world.
The term originated in military strategy, where one team (“red”) challenged the plans of a defending force (“blue”) to improve defense readiness. Today, this practice extends beyond cybersecurity to test AI’s technical, ethical, and social resilience before and after deployment.
Effective AI red teaming depends on the diversity and expertise of the team itself. Interdisciplinary and demographically varied participants can identify harms that might otherwise go unseen, reflecting the social contexts in which AI operates. Red teaming may include general users, domain experts, or even generative AI systems working alongside humans to stress-test safeguards and uncover risks.
In AI ethics and law, red teaming supports the protection of human rights such as privacy, equality, and safety. By anticipating how AI systems could cause harm, through bias, manipulation, or inadequate oversight, red teaming fulfills an ethical obligation to prevent foreseeable harm. Responsible red teaming also promotes accountability and transparency, ensuring AI systems respect human dignity and operate within lawful and moral boundaries.
For further study
National Institute of Standards and Technology, Artificial Intelligence Red-Teaming: A NIST Concept Paper (NIST AI 600-1, 2024).