Can You Really Trust AI? Red Teaming Is Helping Us Find Out

As AI systems grow more advanced, so do the risks that come with them. From hallucinated answers to unintended bias and even security breaches, AI models, especially generative and multimodal ones, are being deployed faster than we’re able to secure them. That’s where red teaming comes in.

Originally used in military and cybersecurity operations, red teaming is now being adapted to test the limits of AI. Its purpose? To uncover vulnerabilities before they’re exploited in the real world.

At its core, red teaming is about role reversal. By thinking like attackers, testers can simulate adversarial prompts, manipulations, or misuse scenarios. These could range from inputting cleverly worded prompts to get around content filters, to simulating multi-step attacks that blend text, code, or even images. In fact, multimodal red teaming is gaining traction as AI tools become more capable across formats, including audio, video, and software.

There are both automated and manual approaches. Automated red teaming might use large datasets or AI-generated prompts to simulate thousands of attack vectors quickly. Manual red teaming, on the other hand, relies on creative, human-led scenarios that are harder to predict or script, such as layered roleplay prompts that bypass filters over time.

While red teaming won’t make AI perfectly safe, it offers an evolving, structured way to measure and manage risk. It also supports compliance with upcoming regulations like the EU AI Act, which requires systems to prove safety and resilience. Ultimately, red teaming isn’t just a technical fix. It’s a mindset shift: assume your AI can be misused and test for it before someone else does.

Appanderanda, Mandanna. Kaushal, Rathi. 2025. “What Is Red-Teaming and How Can it Lead to Safer AI?” World Economic Forum. June 16.

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Can You Really Trust AI? Red Teaming Is Helping Us Find Out