AI red teaming

By Sam Rivera, Founder, SentinelPanda · June 19, 2026 · 1 min read · AI Governance

Red teaming is penetration testing for model behaviour — deliberately trying to make the AI do the things it should not.

What it is

AI red teaming is the practice of adversarially testing an AI system to find ways it behaves harmfully, unsafely, or against intent — generating disallowed content, leaking data, being manipulated past its guardrails, or producing biased or dangerous outputs. It is penetration testing aimed at model behaviour rather than infrastructure.

Why it matters

Models fail in ways traditional testing misses — a system can be perfectly secure at the infrastructure level and still be coaxed into harmful outputs. Red teaming surfaces these behavioural failures before real users or attackers do, and is becoming an expectation for high-capability and high-risk systems.

What it probes

Jailbreaks and prompt injection that bypass guardrails; data leakage (extracting training data or system prompts); harmful, biased, or dangerous content; and misuse scenarios specific to your application. The findings are concrete behaviours to mitigate, not abstract risks.

Feed it into governance

Red-team findings should drive mitigations (better guardrails, filtering, monitoring) and feed the system's risk assessment and documentation — and you re-test after changes, like any security control. SentinelPanda tracks red-teaming as a control and stores the findings as evidence for high-risk AI systems.