Evaluate the AI application you plan to deploy—not just the model in isolation. Define the system’s assets, users, data flows and likely threats; turn the important risks into measurable release criteria; then combine controlled testing, adversarial red teaming and, where relevant, user testing. There is no universal score that proves an AI model is secure enough to deploy. Your decision should be tied to the use case, the consequences of failure and the evidence your tests actually produce.
Start with the deployed system and its threat model
Set the boundary of the evaluation before choosing tests. An AI product may include a model, an application, connected services and the environment in which it runs. A model-only assessment can miss a weakness in the application or an integration that changes what an attacker can reach.
Write down the intended use and deployment context, including:
- Who will use the system, and what kinds of access they have.
- What data it receives, stores, retrieves or produces, and which of it is sensitive.
- The model, application, configuration, software, hardware and deployment environment in scope.
- Connected services, tools or agents, where present, and any actions they can take.
- What could happen if information is exposed, outputs or actions are altered, or service is disrupted.
For a generative AI application, account for user-supplied and retrieved content, integrations and downstream actions where they exist. The NIST Generative AI Profile (AI 600-1) treats risks as arising across lifecycle stages and at model, application and ecosystem scope. A threat model should reflect the actual product rather than treating “the model” as the whole system.
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Turn the risks into release criteria
For each significant threat, write an objective that a test can evaluate. Organize objectives around confidentiality, integrity and availability: what must stay private, what must not be changed or triggered improperly, and what needs to remain accessible. NIST identifies these security properties as relevant to AI systems, their data and their supporting software and hardware.
Examples of testable objectives include whether a user can obtain data they are not authorized to see, whether untrusted input can change a protected output or action, and whether an attacker can disrupt the service. These are ways to operationalize security goals, not NIST-published benchmarks. Set the acceptance criteria before testing, and have the teams accountable for the deployment decide how much residual risk is tolerable. A threshold appropriate for an internal low-impact tool may not be appropriate for a system whose failure has serious consequences.
Use complementary evaluation methods
No single test method answers every question. NIST’s ARIA Evaluation Planning Manual (AI 200-3), published September 18, 2026, describes three evidence sources for holistic evaluation: Model Testing, Red Teaming and User Testing.
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Controlled model testing
Use repeatable tests to measure behavior under expected and adversarial inputs. Record the model and application versions, test conditions, scenarios and results so that a fix can be checked against the same cases later. These tests help answer defined questions, but their results apply to the conditions and scope tested.
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Red teaming
Have testers probe realistic attack paths, including interactions among the model, application and connected components. The goal is to find ways a plausible attacker could defeat the system’s protections, not merely to collect unusual model responses. Red-team findings should be reproducible enough to investigate and retest where possible.
User or field testing
Include user testing when interaction patterns, workflow design or human reliance could change the security outcome. For example, a technically correct control may not protect information if users are routinely led to share it in an unsafe way. The relevance of this method depends on how people will actually use the system.
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NIST’s TEVV-Athlon framework describes a customizable approach in which assessment events and tools generate evidence about measurement concepts selected for an organization’s objectives. Its draft covers statistical machine learning, large language models, multimodal systems and agentic systems, among others. It is a structure for tailoring an evaluation, not evidence that a particular model has passed a security test.
Build a threat-based test matrix
Select tests based on your threat model, exposure and potential impact; not every attack applies equally to every system. Include relevant conventional security weaknesses as well as AI-specific concerns. NIST’s AI security and resilience guidance identifies the following areas:
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- Confidentiality: exposure of protected information in training data, outputs, model weights, configuration or connected systems.
- Integrity: unauthorized changes to data, outputs, model behavior, configuration or downstream actions.
- Availability: attacks or weaknesses that make the AI service or supporting components unavailable.
- Evasion: inputs crafted to cause a machine-learning system to behave incorrectly.
- Model extraction: attempts to infer or reproduce a model through access to its outputs.
- Membership inference: attempts to determine whether particular data was used to train a model.
- Application and infrastructure exposure: weaknesses in the software, hardware, deployment environment or integrations around the model.
These are categories to consider, not a checklist that every system must fail or pass in the same way. NIST also cautions that AI has a complex attack surface and that existing frameworks and guidance do not comprehensively address every AI security concern. Document risks that your evaluation cannot cover credibly rather than treating an untested area as safe.
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Keep an evidence trail from objective to decision
For each release objective, preserve enough information for another reviewer to understand what was tested and what the result means. A practical record includes:
- The objective and the threat it addresses.
- System boundary, model and application versions, configuration and test environment.
- Test design, tools, input scenarios and results.
- Severity, reproducibility and limitations of the evidence.
- Mitigations applied, residual risk and the person or team accepting that risk.
- Whether the pre-agreed release criterion passed.
Reassess when a material change to the model, data, configuration, software, integration or deployment environment could alter the result. This lifecycle approach aligns with NIST’s view of AI risk across design, development, deployment, operation and decommissioning; it does not mean every change requires repeating every test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an evaluation approach that fits the decision
Internal testing, an external red team or a combined engagement can each be useful. Compare them against the decision you need to make rather than treating one approach as universally best.
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| Comparison axis | Question to ask |
|---|---|
| Coverage | Does the evaluation cover only model behavior, or also the application, integrations, data flows and deployment environment? |
| Evidence type | Will it produce repeatable controlled-test results, adversarial exploration, user or field observations, or a combination? |
| Independence and expertise | Can the evaluators challenge internal assumptions and address both AI-specific and conventional security risks? |
| Relevance | Do scenarios represent the actual use case, threat model and potential consequences? |
| Reproducibility | Can the organization rerun relevant tests after fixes or material system changes? |
| Decision usefulness | Do the findings map to agreed release criteria, mitigations and named residual-risk owners? |
Bring in outside evaluators when the necessary independence or specialist capacity is unavailable internally. Regardless of who tests the system, define the scope and release criteria first so the results can inform a deployment decision.
Decide what results should block deployment
Use a risk-based release gate rather than a borrowed score. Delay deployment if a high-consequence objective fails, or if a material risk cannot be evaluated credibly and has no acceptable mitigation. Consider restricting exposure, access or functionality when mitigations reduce—but do not eliminate—the risk. Deploy only when release-blocking criteria have passed and remaining risks have documented mitigations and accountable owners who accept them.
These are practical decision options, not a universal NIST gate. NIST’s guidance supports assessments tailored to organizational objectives and context; it does not establish one pass mark that certifies a model as safe.
Check the status of guidance before relying on it
NIST materials can help structure an assessment, but their publication status matters when describing them or using them as policy baselines. As of October 4, 2026, the documents covered here have different statuses:
Quick Recap
- AI Risk Management Framework 1.0: a voluntary framework released January 26, 2023; NIST says it is being revised.
- Generative AI Profile (AI 600-1): published July 26, 2024, as a cross-sectoral companion with suggested actions, including attention to pre-deployment testing. The profile says future revisions may add risks and actions as evidence develops.
- ARIA Evaluation Planning Manual (AI 200-3): published September 18, 2026; it covers planning for model testing, red teaming and user testing.
- TEVV-Athlon: NIST announced an initial public draft on August 7, 2026, with input sought through October 6, 2026. Check whether a later version is available before describing the draft as final.
- Cyber AI Profile (IR 8596): the version reviewed is an initial preliminary draft published December 16, 2025, organized around NIST Cybersecurity Framework 2.0 outcomes.
- NIST IR 8578: a final workshop summary published August 2026. It summarizes governance and operational discussion toward a Cyber AI Profile; it is not the profile itself.
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