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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Build an AI red teaming program around the complete system, its likely risks, and the way it changes over time—not just a prompt test against a model. Start with an accountable risk owner and a defined system boundary, threat-model plausible attacks, combine model testing with adversarial exercises and real-world evaluation, and turn findings into owned mitigations that are retested. Red teaming is one part of a broader AI evaluation and risk-management effort; no single exercise can prove a system safe.
Define what you are protecting and who owns the risk
Before designing tests, name an accountable risk owner and decide which system is in scope. NIST’s voluntary AI Risk Management Framework (AI RMF) supports incorporating trustworthiness into AI design, development, use, and evaluation. Its Generative AI Profile can help organizations identify risks distinctive to generative AI and consider actions suited to their goals and priorities. The published AI RMF 1.0 and profile should be distinguished from any future revision; neither is a substitute for assessing your organization’s particular risks.
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Draw the boundary around the system that people actually use, not just the model. Record its intended use, foreseeable misuse, affected stakeholders, sensitive data, consequential actions, dependencies, and operating environment. Depending on the deployment, the boundary may include the model, application, data flows, connected tools, user interfaces, hosting environment, external APIs, and users. The UK NCSC’s Guidelines for secure AI system development apply to providers building systems themselves as well as those building on other providers’ tools and services.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Assets: What data, capabilities, decisions, accounts, or services would matter if exposed, manipulated, or made unavailable?
- Trust boundaries: Where does information or authority cross between users, the application, the model, tools, external services, and infrastructure?
- Impact: What could happen to people, operations, or the organization if the system behaves incorrectly or is abused?
- Ownership: Who can accept residual risk, assign remediation, and coordinate security, engineering, privacy, and operational response?
Use that context to decide which systems and scenarios deserve the most attention. The aim is not to test everything equally; it is to make coverage proportional to plausible impact and exposure.
#1 Best Overall
Threat-model attacks against the system, not an abstract model
Build scenarios from the assets and trust boundaries you identified. For each scenario, record the attacker’s goal, relevant capability or access, the method they might use, the lifecycle stage involved, and the potential system-specific impact. Include ordinary cybersecurity threats alongside AI-specific ones: AI features do not remove the need to assess application, identity, data, infrastructure, and dependency risks.
NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations provides shared terminology and organizes attacks by methods, lifecycle stage, goals, and attacker capabilities. Its categories include evasion, data poisoning, privacy breaches, trojans, and backdoors, among others. Treat the taxonomy as a vocabulary and planning aid, not an exhaustive checklist or ready-made test plan. Which scenarios matter depends on your system and threat model.
Rank #2
For a generative AI application, consider its actual context and integrations where present. For example, assess the consequences of an attacker influencing information that reaches the model, eliciting sensitive information, or abusing an application’s connected tools—if those data flows or capabilities exist. The scenarios should reflect the system’s real permissions, inputs, outputs, and operating constraints; a generic attack list cannot establish coverage for every architecture.
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Choose complementary evaluation methods
Plan more than one kind of evaluation. NIST’s Assessing Risks and Impacts of AI (ARIA) describes model testing, red-teaming, and field testing as distinct evaluation levels, with an emphasis on technical and contextual robustness rather than accuracy alone. A model-only result does not, by itself, represent the integrated application or the conditions in which people use it.
| Evaluation mode | What it examines | What it can contribute |
|---|---|---|
| Model testing | Model behavior under defined tests. | Repeatable evidence about selected model behaviors; it does not by itself establish how the integrated system behaves in context. |
| Red-teaming | Adversarial probing of a defined target, using scenarios relevant to its threat model. | Evidence about how the target responds to adversarial attempts, including observed impact within the agreed scope. |
| Field testing | AI behavior in a real or representative use context. | Evidence about contextual robustness and risks that isolated model tests may not represent. |
These modes are complementary, not interchangeable. Use repeatable tests where they can track known behaviors, red-team exercises to probe meaningful adversarial scenarios, and field evaluation to learn from the operating context. NIST’s ARIA describes all three; it does not mean that any one level, or a particular result in it, certifies a system as safe.
Set exercise rules before testing
Make the test safe, authorized, and useful by agreeing on boundaries before anyone probes the system. The following are practical rules for an exercise, rather than a single template prescribed by NIST, NCSC, or MITRE:
- Write down the target, in-scope components, excluded systems, allowed techniques, and test window.
- Specify test accounts, data handling, and restrictions on accessing or retaining sensitive information.
- Name escalation contacts and define stop conditions, including what to do if testing affects real users, data, or service availability.
- Agree how evidence and sensitive findings will be stored, shared, and disclosed to the people responsible for remediation.
- Record the environment and configuration so the team can interpret findings and reproduce relevant results.
Ensure the team has authorization for the systems and services it will test, especially when external providers or shared infrastructure are involved. Preserve enough evidence to explain what happened, under what conditions, and why it matters without collecting unnecessary sensitive data.
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A finding is useful when the right people can understand its conditions and impact, decide what to do, and verify whether the change worked. Maintain a record for each finding with:
Best Value
- Reproducible evidence and the conditions under which the behavior occurred.
- The affected system boundary, relevant attacker goal or capability, and plausible impact.
- A severity rationale tied to the organization’s risk context, rather than an unexplained label.
- A remediation owner, a decision or due date set by the organization, and the mitigation status.
- A retest result showing whether the original behavior persists and whether the change introduced other relevant effects.
Route issues to the teams able to address them—such as application engineering, model or data owners, infrastructure, privacy, or operations—and feed unresolved risks into the organization’s risk decisions. A technical change is not a completed fix until the relevant behavior has been checked again in the system context where it matters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Cover the AI system throughout its lifecycle
Red teaming should fit into lifecycle security rather than operate as a one-off event. The NCSC guidance organizes recommendations into secure design, secure development, secure deployment, and secure operation and maintenance. Its advice includes threat modeling and risk understanding during design; supply-chain security and documentation during development; infrastructure protection and incident processes during deployment; and logging, monitoring, and update management during operation.
| Lifecycle stage | Security work to connect with evaluation | Red-team program implication |
|---|---|---|
| Secure design | Understand risks and threat-model the system. | Use the model of assets, trust boundaries, attacker goals, and impacts to select scenarios before implementation decisions become difficult to change. |
| Secure development | Address supply-chain security and maintain documentation. | Keep test scope and findings aligned with the components and dependencies being built or integrated. |
| Secure deployment | Protect infrastructure and prepare incident processes. | Confirm the deployed boundary and escalation path; a test result against a different configuration may not describe the live service. |
| Secure operation and maintenance | Log and monitor behavior, manage updates, and handle incidents. | Use operational signals and material changes to identify when existing assumptions or evaluations need to be revisited. |
MITRE’s AI Red Teaming: Advancing Safe and Secure AI Systems identifies benefits of recurring red teaming through development, deployment, and use. Together, lifecycle coverage and recurring exercises help prevent a successful evaluation at one point in time from being mistaken for a lasting guarantee.
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Set a cadence that responds to risk and change
Set the schedule according to the system’s risk, exposure, and organizational capacity rather than adopting a universal interval. Reassess when a material change could invalidate earlier assumptions, such as a change to the model, application, data, integrations, threat context, or operating environment, and after relevant incidents. The decision to trigger a targeted retest or a broader evaluation should follow the scope and likely impact of the change.
Keep a program record that makes coverage and follow-through visible: the system boundary and owner, threat-model scenarios, evaluation modes used, findings and risk decisions, mitigation owners, and retest outcomes. Neither the cited guidance nor ARIA establishes a universal test calendar, team size, budget, or pass threshold. Define those locally and explain the risk rationale behind them; do not treat a pass score as proof that the system is safe.
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