Build AI red teaming into a broader, risk-based security program: define what the whole system does and who could be harmed, model threats across its lifecycle, combine model tests with adversarial exercises and real-world evaluation, then assign owners to findings and retest after fixes or material changes. A prompt test alone cannot establish that a deployed AI system is safe.
What an AI red teaming program should cover
AI red teaming is a way to probe a system adversarially, not a substitute for security engineering, risk management, or every other form of evaluation. The object of assessment is often larger than the model: it may include the application, data, tools, interfaces, hosting, external services, users, and operating environment. A finding matters because of what an attacker could achieve in that context—not simply because a model produced an undesirable answer in isolation.
Start with organizational risk context. The NIST AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. NIST’s Generative AI Profile can help organizations identify distinctive generative AI risks and consider actions aligned with their goals and priorities. The published AI RMF 1.0 and profile are the available guidance; NIST describes the framework as being revised, so do not treat a future revision as already-published requirements.
Use the framework to organize decisions, not to claim certification or proof of safety. Set the program’s scope and testing effort according to the system’s purpose, exposure, dependencies, potential impact, and organizational risk tolerance.
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Set the system boundary and accountable owner
Name an accountable risk owner before testing begins. That person should be able to convene security, engineering, product, operations, privacy, and relevant business stakeholders, make or escalate risk decisions, and ensure findings receive resources and follow-through.
Document the system boundary in terms of components and relationships, not just a model name. Record what is intended, what can plausibly be misused, and what a successful attack could affect.
- Components: model and version, application code, prompts or policies, retrieval and other data stores, connected tools, APIs, interfaces, hosting, and external providers.
- Flows and trust boundaries: what enters and leaves the system, where data is stored or transformed, and which components can call or influence others.
- People and use: intended users, administrators, affected stakeholders, access levels, and the operating environment.
- Impact: sensitive information, consequential actions, business services, safety or rights implications, and plausible misuse.
- Dependencies: third-party models, services, datasets, software, and operational processes that could affect security.
This boundary is also a practical way to avoid a common gap: testing the model while excluding the application and integrations through which a real user or attacker interacts with it. The UK NCSC’s secure AI guidance is intended for providers that build systems themselves and those that build on other providers’ tools and services.
Map threats to the lifecycle
Use a threat model to connect assets and trust boundaries to attacker goals, capabilities, and attack methods. Include conventional cybersecurity risks alongside AI-specific ones. NIST’s adversarial machine learning taxonomy offers shared terminology and organizes attacks by method, lifecycle stage, goal, and attacker capability; it is a vocabulary resource, not an exhaustive, ready-made plan for every system. Its categories include evasion, data poisoning, privacy breaches, trojans, and backdoors.
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Choose scenarios because they fit the system boundary and threat model. For a generative application with retrieval and connected tools, for example, an assessment might examine whether untrusted content can influence system behavior, whether sensitive records can be exposed, or whether a tool can take an action beyond the user’s authorization. These are illustrative questions, not a universal scenario checklist. The right scenarios depend on architecture, data, access, and consequences.
Cover the system from design through operation. The NCSC divides secure AI development guidance into four lifecycle areas:
| Lifecycle stage | Security work to include | Red-team program implication |
|---|---|---|
| Secure design | Understand risk and threat model the system. | Define the boundary, assets, trust relationships, likely attackers, and meaningful harm before deciding what to test. |
| Secure development | Protect the supply chain and document the system. | Keep dependencies, data and system changes visible so test results can be tied to the version and configuration assessed. |
| Secure deployment | Protect infrastructure and establish incident processes. | Set safe test boundaries and know how to escalate a finding that affects a live service or sensitive data. |
| Secure operation and maintenance | Use logging, monitoring, and update management. | Use operational evidence and changes to inform reassessment, incident response, and retesting. |
The lifecycle areas follow the NCSC Guidelines for secure AI system development. Testing should not end at a development milestone if deployment, updates, or changed usage can alter exposure.
Choose complementary evaluation modes
Plan model testing, red teaming, and field evaluation as distinct but complementary activities. NIST’s ARIA program describes these three evaluation levels and focuses on technical and contextual robustness, not accuracy alone. Each mode answers a different question:
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| Evaluation mode | Primary object and setting | Useful evidence | What it cannot establish alone |
|---|---|---|---|
| Model testing | Model behavior under defined, repeatable tests. | Observed behavior for the tested prompts, inputs, model, and configuration. | How the integrated application, its tools, users, or operating context behave. |
| Red-team exercise | Adversarial probing of a scoped system or component. | Observed attack paths, conditions, impact, and reproducible evidence. | That all possible attacks or harms have been found, or that the system is safe. |
| Field testing | AI system behavior in a more realistic deployment or use context. | Contextual evidence that may not appear in isolated model tests. | That every user, environment, or future system version will behave the same way. |
The comparison reflects NIST ARIA’s evaluation structure. A sound plan selects methods based on the question and risk. Repeatable model tests can help track behavior across changes; a red-team exercise can explore attacker paths through the scoped system; field evaluation can reveal contextual risks. None replaces the others in every situation.
Design the program as a repeatable workflow
1. Prioritize risks and define objectives
Translate the organization’s risk assessment into a short set of test objectives. For each objective, state the asset or stakeholder at risk, attacker goal, relevant capability, system component, and possible impact. Prioritize scenarios that could expose sensitive data, bypass important controls, misuse an integrated capability, disrupt a critical service, or otherwise create material consequences for the actual system.
Use NIST’s attack taxonomy to name relevant methods and lifecycle stages, then tailor them to the architecture. A taxonomy category is a prompt for analysis, not a mandate to test every category or evidence that every category applies.
2. Write a scoped test plan
For each scenario, identify the target version and configuration, interfaces in scope, test accounts and data, assumptions, and what result would count as a meaningful finding. Specify exclusions and escalation routes. A useful plan ties each activity to an objective rather than treating a large volume of prompts or test cases as proof of coverage.
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3. Run the exercise safely
Before testing, confirm written authorization and practical rules of engagement. The cited guidance supports risk management, lifecycle security, and incident processes, but it does not prescribe one universal red-team rules-of-engagement template. As an operational safeguard, agree on:
- Authorized systems, accounts, time windows, and test data.
- Actions that are prohibited, such as accessing real customer data or causing disruption beyond the agreed scope.
- Stop conditions, including unexpected access to sensitive data, service impact, or evidence of an active incident.
- Named escalation contacts and a way to report urgent findings securely.
- How logs, screenshots, prompts, outputs, and other potentially sensitive evidence will be stored and shared.
Use test data and accounts where possible, and avoid escalating access or impact beyond what is authorized. The aim is to establish whether a risk exists and its conditions, not to create unnecessary harm while proving it.
4. Preserve evidence and assess impact
Capture enough detail for another authorized reviewer to understand and reproduce the finding. Record the system and model versions, configuration, relevant inputs and outputs, sequence of actions, account permissions, observed behavior, and test limitations. Protect evidence that contains sensitive material.
Assess severity in context. A behavior that looks concerning in a model-only test may have little impact if the system has no relevant access; a narrow behavior may be serious if it enables a consequential action or exposes protected data in the integrated application. Document the rationale rather than relying on a score without its assumptions.
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5. Assign remediation and verify the change
Give each finding an owner, a decision, and a target for follow-up appropriate to its risk. Possible outcomes include mitigation, additional engineering work, acceptance by an authorized risk owner, or further investigation. Record the evidence supporting the decision.
Retest after a mitigation to check whether it addresses the original conditions and whether it creates a new weakness elsewhere in the system. Send relevant findings into engineering and operational risk processes; deployment incident management, monitoring, and updates are part of the NCSC lifecycle guidance.
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Set the testing schedule to fit the system’s risk and rate of change. Reassess when a material change affects the model, application, data, tools, integrations, users, deployment environment, or threat context, and after relevant incidents. A small change may need a targeted regression test; a change to a trust boundary or consequential capability may justify revisiting the threat model and broader exercise scope.
MITRE describes benefits of recurring AI red teaming through development, deployment, and use. That supports a continuous program rather than a one-time launch check, but the reviewed sources do not establish a universal interval, team size, budget, or pass threshold. Set those as organization-specific policy based on risk, capacity, and change—not as a purported standard.
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What a credible result can—and cannot—say
A red-team report should describe the tested system boundary, version, objectives, methods, conditions, evidence, impact assessment, limitations, decisions, owners, and retest status. Findings can support decisions about mitigations and residual risk. A clean test result means the exercise did not identify a relevant issue under its scope and conditions; it does not prove that the system is safe or that untested attack paths do not exist.
The program’s value comes from connecting adversarial testing to the real system and its lifecycle: define risks, test the right boundaries, preserve evidence, act on findings, and reassess when the system or context changes.
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