Red teaming

A structured adversarial testing exercise where a team attempts to find vulnerabilities, flaws, and failure modes in an AI system by simulating real-world attacks and misuse scenarios.

Source: AIEOG AI Lexicon (Feb 2026), adapted from NIST AI 100-2e2025 and White House EO 14110

Red teaming is the practice of intentionally trying to break, trick, or misuse an AI system in a controlled setting to discover its weaknesses before adversaries do. A red team acts as a simulated attacker, probing the system for vulnerabilities, biases, safety failures, and unintended behaviors.

The concept originated in military and cybersecurity contexts, where red teams simulate enemy attacks to test defenses. In AI, red teaming has been adapted to test generative AI systems, LLMs, and other AI applications for prompt injection vulnerabilities, safety filter bypasses, bias and discrimination, harmful content generation, information leakage, and unexpected behaviors.

Red teaming is distinct from standard testing because it is adversarial — the goal is to actively find ways to make the system fail, not to confirm it works. This mindset shift is important because real-world adversaries will not follow expected usage patterns.

Financial institutions deploying AI face sophisticated adversaries — fraudsters, social engineers, and threat actors who will probe AI systems for exploitable weaknesses. Red teaming helps institutions discover vulnerabilities before adversaries do.

Red teaming is also increasingly expected by regulators and governance frameworks. The NIST AI RMF includes adversarial testing as a core governance function. The White House EO 14110 emphasizes red teaming for AI safety. Examiners may ask about adversarial testing as part of AI governance assessments.

For customer-facing AI (chatbots, virtual assistants), red teaming can identify scenarios where the system provides harmful advice, discloses confidential information, produces discriminatory responses, or behaves in ways that create regulatory exposure.

1. Conduct red teaming before deployment. Test AI systems adversarially before they go live, not after incidents occur.
2. Use diverse red team members. Include people with different backgrounds, perspectives, and expertise to identify a wider range of vulnerabilities.
3. Test for domain-specific risks. Financial services red teams should test for regulatory compliance failures, fair lending violations, and data privacy breaches, not just generic safety issues.
4. Document findings and remediation. Record all vulnerabilities discovered, their severity, and the remediation steps taken.
5. Conduct ongoing red teaming. AI systems evolve over time. Red teaming should be repeated periodically, especially after significant changes.
6. Include in governance frameworks. Red teaming should be a standard component of AI validation and governance.

Adversarial AI, Prompt injection, Guardrails, Validation, AI model/system exploitation, Robustness