Red teaming

Definition

The practice of systematically probing an AI system to find vulnerabilities, biases, and failure modes before deployment. Red teaming is now standard practice at major AI labs and increasingly required by regulation.

Why it matters

Red teaming is how you find out what your AI will do before your users do. Every model has failure modes: edge cases that produce harmful outputs, biases that emerge in specific contexts, and vulnerabilities that can be exploited. Finding these before deployment is dramatically cheaper and less damaging than discovering them in production. Red teaming has evolved from ad hoc testing to a structured discipline with dedicated teams, standardized methodologies, and third-party auditors. The US government and EU regulators now require red teaming for certain AI systems. Companies that skip red teaming are not saving money; they are accumulating risk that will eventually materialize as incidents.

In practice

Anthropic red-teams every Claude release against a comprehensive threat taxonomy including harmful content generation, bias, and capability elicitation. OpenAI published its red teaming practices and engaged external security researchers. The AI Safety Institute (AISI) in the UK conducted independent red teaming of frontier models. Major AI labs maintain dedicated red teams of 10-50 people. Third-party red teaming services from companies like HackerOne and Trail of Bits have expanded into AI. The methodology typically involves: defining threat scenarios, creating adversarial test cases, running systematic evaluations, documenting findings, and verifying that mitigations work.