Red Teaming (AI)

The premise of AI red teaming is disarmingly simple: try to break your AI before someone else does. In practice, it is a disciplined, multi-dimensional evaluation discipline that tests far more than raw safety. A comprehensive AI red team exercise probes for: **harmful content generation** (instructions for weapons, self-harm, illegal activity); **jailbreaks and prompt injection** (adversarial inputs designed to bypass system prompts and safety filters); **privacy leakage** (extraction of training data, PII, or confidential information from the system context); **bias and discrimination** (disparate treatment of demographic groups); **policy violations** (outputs that violate company policy, regulatory requirements, or contractual obligations); and **agentic risks** (for agent systems, testing whether an attacker can cause the agent to execute unauthorized actions).

The practice has two complementary modes. **Manual red teaming** uses skilled human testers — ideally with domain expertise in the deployment context (financial regulation, healthcare, child safety) — who apply creative reasoning to find vulnerabilities that automated systems miss. **Automated red teaming** uses adversarial AI systems, classifier-guided fuzzing, or systematic prompt mutation to generate thousands of test cases at scale. Best-practice enterprise deployments use both: automated red teaming for coverage breadth, human red teaming for depth and creative attack generation.

The EU AI Act mandates red teaming for high-risk AI systems under Article 9 risk management requirements. The US AI Safety Institute has published red teaming guidelines for frontier models. Enterprise buyers of third-party AI should require red team reports as part of vendor due diligence, and should conduct their own application-layer red teaming to cover risks specific to their deployment context — risks the model provider's red team cannot anticipate.