Red Teaming for AI is the structured adversarial evaluation where a team systematically tries to make the model fail, produce harmful outputs, or behave unexpectedly — proactively discovering vulnerabilities, biases, and failure modes before deployment.

Red Teaming Approaches

• Manual: Human red teamers craft inputs designed to expose model weaknesses.
• Automated: Use other ML models (red team LLMs) to generate adversarial prompts.
• Structured: Follow a taxonomy of potential failure modes and systematically test each category.
• Domain-Specific: In semiconductor AI, test with physically implausible inputs, edge-case recipes, and adversarial sensor data.

Why It Matters

• Pre-Deployment Safety: Discover dangerous failure modes before the model is in production.
• Security: Identifies potential adversarial attack vectors that could be exploited.
• Trust: Demonstrates due diligence in model safety — increasingly required by AI governance frameworks.

Red Teaming is the authorized attack team — systematically trying to break the model to improve it before real users encounter the same failures.