AI Red Teaming is a structured approach to testing AI systems by simulating adversarial attacks and abuse scenarios to identify vulnerabilities, biases, and potential harms before deployment. Borrowed from traditional cybersecurity red teaming, it adapts adversarial thinking specifically for AI systems.
AI red teaming activities include: testing for prompt injection vulnerabilities, attempting to extract training data or system prompts, probing for harmful or biased outputs, testing content safety filters and guardrails, evaluating robustness against adversarial inputs, assessing privacy protections, and identifying potential misuse scenarios.
The practice has gained prominence with the rise of large language models, where organizations like OpenAI, Google, Anthropic, and Microsoft conduct extensive red teaming before releasing new models. The Biden Administration's Executive Order on AI (2023) and the EU AI Act both reference adversarial testing requirements.
Effective AI red teaming requires diverse teams with varied perspectives, systematic methodologies covering multiple risk categories, documentation of findings and remediation actions, regular retesting as models are updated, and integration with broader AI governance and risk management processes.
