AI red-teaming

AI red-teaming is the practice of deliberately probing an AI system for weaknesses by adopting an adversarial mindset — trying to make the system behave in ways it should not. It borrows the concept from security testing, where a red team attacks a system to expose flaws a defender can then fix. Applied to AI, it targets both conventional security issues and the failure modes specific to machine-learning systems.

The scope is broad. Red-teamers test whether a model can be manipulated through prompt injection or jailbreaks, whether it can be induced to leak training data or system instructions, whether it produces harmful, biased or unsafe outputs, and whether the surrounding system — its tools, data access and integrations — can be abused. The aim is to understand realistic failure paths under adversarial pressure, not just to confirm the system works when used as intended.

Red-teaming is most valuable when it is systematic and repeatable rather than a one-off exercise: documented scenarios, a record of what was tried and what succeeded, and findings that feed back into design and controls. For deployments in sensitive settings it is an important part of demonstrating that risks have been actively sought out and addressed, which supports the governance and assurance work that regulated environments require.