Definition: AI red teaming is structured adversarial testing in which people or automated tools try to make an AI system fail, leak data or behave unsafely, so weaknesses are found and fixed before attackers or users find them.
Red teamers attempt prompt injection, jailbreaks, data extraction, harmful content, biased outputs and misuse of connected tools. Automated scanners generate large numbers of attack prompts, while human testers explore creative and domain-specific scenarios. Findings are logged with reproduction steps and severity.
AI systems fail in ways traditional security testing does not cover, especially AI agents that read external content and take actions. Guidance such as the NIST AI Risk Management Framework and the OWASP Top 10 for LLM Applications treats adversarial testing as part of managing AI risk.
Teams red team before launch and after major changes, turn successful attacks into regression tests, and strengthen guardrails, permissions and monitoring based on what they find.
Ryz security engineers and AI engineers test agents and LLM applications before and after launch, working on your team.
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