Red Teaming
The practice of deliberately trying to find flaws, vulnerabilities, and harmful behaviors in AI systems before they're deployed.
Why this matters
Red teaming comes from military and cybersecurity traditions where you have a dedicated team trying to break your own defenses. In AI, it means hiring people to find all the ways a model can go wrong before users discover them in the wild. It's basically stress-testing for bad behavior.
The work involves creative adversarial thinking. Red teamers try to get models to produce harmful content, leak private information, give dangerous instructions, or behave in unexpected ways. They probe edge cases, try unusual prompts, and generally act like motivated attackers. Good red teaming finds problems you'd never think of on your own.
Companies have gotten more serious about this as AI systems get more capable. OpenAI, Anthropic, and Google all run extensive red teaming programs before major releases. Some bring in outside experts. Domain specialists, security researchers, people who understand specific risks like bioweapons or cyberattacks. Fresh eyes catch things internal teams miss.
Red teaming isn't foolproof. No matter how thorough you are, users in the real world will find novel attacks you didn't anticipate. But it's still valuable. Catching 90% of problems before launch is way better than catching 0%. It's become a standard part of responsible AI development, and models are genuinely safer because of it. The goal isn't perfection, it's reducing risk to reasonable levels.