Category: Adversarial Security

Red Teaming

Also known as: Red Team, AI Red Teaming

Simply put

Red teaming is a practice in which authorized specialists deliberately simulate real-world attacks or adversarial behavior against an organization's systems to test their defenses and uncover weaknesses before genuine attackers do. Originally rooted in cybersecurity, the approach is increasingly applied to AI systems to probe for vulnerabilities. It is a testing method intended to reveal problems, not a guarantee that a system is secure.

Formal definition

As commonly defined in cybersecurity, red teaming is a structured, adversarial testing process in which a group of authorized professionals simulates a real-world adversary—attempting physical or digital intrusion at the direction of the target organization—to evaluate the effectiveness of defenses, identify vulnerabilities, and improve incident response. Applied to AI systems ("AI red teaming"), it refers to structured adversarial testing designed to surface vulnerabilities in a model or AI application before they can be exploited. The evidence here scopes red teaming to adversarial and cybersecurity-oriented testing; it does not, on its own, establish red teaming as a formal regulatory requirement, and the precise methodologies and scope for AI systems continue to evolve.

Why it matters

Red teaming addresses a fundamental limitation of routine testing: systems that pass functional checks and standard quality assurance can still harbor exploitable weaknesses that only surface under deliberate adversarial pressure. By authorizing specialists to simulate the behavior of a genuine attacker, an organization gains evidence about how its defenses hold up against realistic threats rather than against idealized or benign use. For AI systems in particular, where inputs can be manipulated in unexpected ways, structured adversarial testing is increasingly used to surface vulnerabilities before they can be exploited in production.

For governance and risk functions, red teaming produces a different class of information than conventional validation or performance testing. It is oriented toward discovering what can go wrong under adversarial conditions, which supports risk identification and can inform incident response planning. It is important to treat red teaming as a method that reveals problems, not as a certification that a system is secure; a red team engagement that finds few issues does not prove the absence of vulnerabilities, and its value depends on the scope, skill, and authorization boundaries of the exercise.

Professionals should also be careful not to overstate the regulatory standing of AI red teaming. The evidence here scopes the practice to adversarial and cybersecurity-oriented testing and does not, on its own, establish it as a formal regulatory requirement. The methodologies and scope for applying red teaming to AI systems continue to evolve, so organizations relying on it should document what was and was not tested rather than treat any single engagement as comprehensive.

Who it's relevant to

AI governance and risk teams

Governance and risk functions use red teaming as a source of risk-identification evidence, learning how AI systems and their defenses behave under deliberate adversarial pressure. They should treat findings as inputs to risk assessment and incident response planning, and should document scope and limitations rather than interpret a clean result as proof that a system is secure.

Security professionals and ethical hackers

Authorized security specialists conduct the simulated attacks that make up a red team engagement. They plan and execute intrusion attempts within agreed authorization boundaries, evaluate the effectiveness of defenses, and report vulnerabilities so the organization can remediate them and strengthen incident response.

Model owners and AI application teams

Teams responsible for models and AI applications benefit from adversarial testing that surfaces vulnerabilities before deployment or exploitation. Because AI red teaming methodologies continue to evolve, these teams should be precise about what was tested and should not assume that adversarial testing substitutes for validation, verification, or ongoing performance monitoring.

Auditors and compliance officers

Auditors and compliance professionals may review red teaming as part of evaluating an organization's testing and control practices. They should verify the scope, authorization, and follow-up on findings, while recognizing that the evidence here does not establish red teaming as a formal regulatory requirement and that its treatment can vary by sector and jurisdiction.

Inside Red Teaming

Adversarial Probing

The deliberate crafting of inputs, prompts, or scenarios intended to elicit harmful, unsafe, non-compliant, or otherwise undesirable model outputs. This is the core activity of red teaming and is distinct from routine functional testing, which typically checks expected behavior rather than actively seeking failure modes.

Threat Modeling Scope

The definition of what harms, misuse cases, or attack vectors the exercise targets (for example, jailbreaks, prompt injection, data exfiltration, or generation of prohibited content). The scope should be documented up front so results can be interpreted against a stated objective rather than treated as exhaustive coverage.

Red Team Composition

The set of participants conducting the exercise, which may include internal specialists, independent testers, or external experts. Composition affects independence and the range of perspectives brought to bear; in some governance frameworks red teaming performed independently of the development team supports second- or third-line-of-defense objectives, though this varies by organization.

Findings and Severity Assessment

The recording of identified vulnerabilities along with an assessment of their significance. As commonly practiced, findings are triaged so that remediation can be prioritized, and results feed into broader risk identification rather than serving as a pass/fail certification.

Remediation and Follow-up

The process by which identified issues are addressed through mitigations, retraining, guardrails, or policy changes, and by which the effectiveness of those measures is re-tested. Red teaming is typically iterative rather than a one-time gate.

Common questions

Answers to the questions practitioners most commonly ask about Red Teaming.

Is red teaming the same as standard model validation or QA testing?

No. Red teaming is typically an adversarial exercise designed to actively probe for failure modes, harmful outputs, or exploitable weaknesses by simulating an adversary or misuse scenario. Standard validation and QA generally assess whether a system meets defined requirements and performs as intended under expected conditions. Red teaming is often complementary to validation rather than a substitute for it, and the two serve different objectives. Note that the specific meaning can vary by organization and sector.

Does passing a red team exercise mean an AI system is safe or free of risk?

No. Red teaming can surface weaknesses that were tested for, but it cannot demonstrate the absence of untested vulnerabilities. As commonly understood, it reduces or helps characterize risk rather than eliminating it. Results are bounded by the scope, techniques, and adversarial assumptions applied, so a system that performs well against one exercise may still fail under different attack strategies or in different deployment contexts.

Who typically conducts red teaming, and should it be internal or external?

Red teaming may be performed by internal teams, external specialists, or a combination, depending on the organization and its objectives. Internal teams often have deeper system knowledge, while external parties may offer greater independence and fresh adversarial perspectives. Some organizations position red teaming to preserve independence from the teams that build the system, though practices vary and there is no single required arrangement across contexts.

How is red teaming scoped for an AI system?

Scoping typically involves defining the objectives, the categories of harm or failure to be probed, the techniques permitted, the system components in range, and any constraints or rules of engagement. Because outcomes are bounded by scope, documenting what was and was not tested is generally important so stakeholders understand the limits of any conclusions drawn. Appropriate scope often depends on the system's use case and risk profile.

How should red teaming findings be documented and acted upon?

Findings are commonly documented with the methods used, issues identified, their potential severity, and recommended mitigations, so that they can be tracked and remediated. Many organizations route findings into existing risk or issue-management processes and revisit them over time. Documentation that captures both what was tested and what was out of scope helps support later review and accountability.

How often should red teaming be repeated?

Frequency typically depends on factors such as changes to the model or system, shifts in the deployment context, emergence of new attack techniques, and the system's risk profile. Because model behavior and adversarial methods can change over time, some organizations treat red teaming as a recurring rather than one-time activity. There is no universally fixed cadence, and appropriate frequency varies by context and applicable expectations.

Common misconceptions

Red teaming is the same as model validation or verification.

Red teaming is an adversarial testing technique aimed at surfacing failure modes and misuse potential. Validation, as commonly defined, is a broader evaluation of whether a model is sound and fit for its intended purpose, and verification checks that a system was built to specification. Red teaming can inform validation but does not substitute for it.

Passing a red teaming exercise means the model is safe or free of vulnerabilities.

Red teaming reduces and helps characterize risk; it does not eliminate it. An exercise is bounded by its defined scope, the creativity and time of the testers, and the threat model used. Absence of discovered issues indicates the tested scenarios did not surface problems, not that none exist.

Red teaming is a one-time requirement completed before deployment.

Because model behavior, usage patterns, and adversarial techniques evolve, red teaming is typically treated as a recurring activity. A single pre-deployment exercise does not account for changes introduced after release or newly discovered attack methods.

Best practices

Define and document the threat model and scope before testing so results can be interpreted against a stated objective and known gaps are explicit.

Where feasible, involve testers who are independent of the development team to strengthen objectivity and align with governance oversight roles.

Record findings with a severity or significance assessment and feed them into the organization's broader risk identification and remediation processes rather than treating the exercise as a pass/fail gate.

Re-test after mitigations are applied to confirm that remediations are effective and do not introduce new failure modes.

Treat red teaming as a recurring activity that is repeated as models, usage, and adversarial techniques change over time.

Clearly communicate the limitations of each exercise, including what was out of scope, so stakeholders do not overinterpret an absence of findings as evidence of overall safety.