Red teaming in AI refers to the structured process of actively probing systems for vulnerabilities, blind spots, or unintended behaviours—often simulating adversarial or high-risk conditions. It is an advanced assurance technique used to challenge assumptions, identify gaps, and stress-test AI systems beyond standard testing protocols.

Unlike conventional testing, red teaming adopts the mindset of an adversary or critical stakeholder. It asks: what happens when the AI system is pushed beyond its design limits, exposed to manipulation, or faced with edge-case scenarios? This is particularly valuable in defence, security, and critical infrastructure applications, where failure could result in significant harm.

Red teaming exercises may involve:

• Crafting adversarial inputs that bypass filters or confuse classifiers

• Simulating misuse or ethical failure modes

• Conducting role-play scenarios to assess human-AI interaction under stress

• Evaluating resilience to social engineering or prompt injection attacks

Assurance teams use red teaming to uncover:

• Hidden biases or unanticipated model behaviours

• Systemic weaknesses in data, logic, or control flow

• Gaps in monitoring, oversight, or escalation protocols

• Inadequate preparation for rare but catastrophic events

Red teaming is especially important for generative models, autonomous agents, and decision-support systems used in volatile environments. It surfaces failures that may not appear during routine validation and helps organisations understand not only whether a system works, but how it might fail.

The outputs of red teaming inform mitigation strategies, governance improvements, and model retraining. Documentation from these exercises also supports audit trails and regulatory reporting.

As a best practice, red teaming should be independent, iterative, and multidisciplinary — bringing together technical experts, domain specialists, and operational users to test assumptions and challenge system boundaries.