Evaluate the complete AI system in the setting where people will use it—not just the model in isolation. Define its intended use and affected users, identify plausible harms, test normal and adversarial behavior, decide whether remaining risks are acceptable, and prepare monitoring and response before launch. No test suite can guarantee safety, and there is no universal launch threshold: the decision depends on the system’s context and the organization’s risk tolerance.
1. Define what you are actually deploying
Start by drawing the deployment boundary. A model may be only one part of the system people encounter: prompts, retrieval sources, connected tools, filters, user interfaces, human review, and downstream decisions can all change its behavior and impact. Assess that integrated system, not only a model card or benchmark score. NIST’s voluntary AI Risk Management Framework treats risk management as work across design, development, deployment, use, and evaluation.
Write down the following before testing:
- System components: model and version, prompts or instructions, data sources, tools, filters, interfaces, and human checkpoints.
- Intended and foreseeable uses: what users are expected to do, and plausible ways they may use the system outside that purpose.
- People affected: direct users and anyone whose opportunities, safety, privacy, or treatment could be affected by its outputs.
- Operating conditions: expected inputs, languages, workload, access controls, environment, and what happens when a dependency or human review step is unavailable.
- Data flows and authority: what information enters or leaves the system, where it goes, and whether outputs merely inform people or trigger consequential actions.
This boundary keeps evaluation tied to real use. For example, a writing assistant that suggests text has a different risk profile from a system whose generated recommendation feeds a high-impact decision, even if both use the same base model.
2. Assign risk ownership and decision authority
Before results arrive, identify who can investigate risks, require mitigations, pause deployment, respond to incidents, and formally accept any remaining risk. A review without an accountable decision-maker can identify problems without resolving whether launch should proceed.
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NIST’s AI RMF Playbook offers voluntary implementation suggestions organized as Govern, Map, Measure, and Manage. These functions can help teams assign work; they are not a certification or proof that a system is safe. Record the decision owner and escalation route alongside the system definition.
3. Map harms that fit this system and setting
List harms that could arise from the model, its integration, user behavior, or downstream reliance on its outputs. NIST describes trustworthiness characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness with harmful bias. Which deserve the most attention—and how they trade off—depends on the use context, as its AI RMF FAQs explain.
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- Unsafe or unreliable outputs: incorrect, unsupported, or misleading answers that could lead to harmful action or misplaced confidence.
- Bias and unequal impact: different error rates or harmful treatment across relevant people or groups.
- Privacy: disclosure, retention, or inappropriate use of personal or confidential information.
- Security and misuse: attempts to bypass safeguards, extract sensitive information, or use the system for harmful purposes.
- Content and rights: hateful or violent output, or potential intellectual-property infringement.
- Operational failure: errors in connected tools, degraded performance, or behavior beyond the model’s knowledge limits.
- Human and downstream effects: overreliance, inadequate review, or an output being used as the basis for a decision that affects someone.
These are prompts for analysis, not a checklist that every application must treat identically. Prioritize harms by severity, likelihood, exposure, and the ability to detect or reverse them in this particular deployment.
4. Turn each material risk into an evaluation plan
For every priority harm, specify a test scenario, the evidence you will collect, what outcome is unacceptable, and what result requires escalation. Set those criteria before examining test results so the team is not tempted to redefine success after seeing a failure.
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For a generative AI system, NIST’s 2024 cross-sector Generative AI Profile, AI 600-1, calls attention to output validity and safety, harmful bias, privacy violations, intellectual-property infringement, violent or hateful content, misuse, and attempts to circumvent safeguards. The profile’s guidance is available in the NIST publication. Tailor scenarios to the product: a general-purpose chat interface, an internal knowledge assistant, and a tool-enabled agent do not expose users or data to the same failure modes.
A useful test-plan record for each risk includes:
- the harm and people potentially affected;
- the scenario, input conditions, and system configuration being tested;
- the measure or observable evidence, including how failures will be reviewed;
- the unacceptable outcome or escalation condition;
- the mitigation, its owner, and how it will be checked; and
- the residual risk remaining after mitigation.
5. Test the model, attack surface, and use context
Ordinary performance tests answer only part of the safety question. A strong plan uses the evaluation levels appropriate to the likely harms, then connects their results to the integrated deployment. NIST’s Assessing Risks and Impacts of AI (ARIA) describes model testing, red-teaming, and field testing, with attention to technical and contextual robustness.
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| Evaluation level | What it examines | What it can miss on its own |
|---|---|---|
| Model testing | Behavior on defined test cases and measures, such as accuracy or safety-related output conditions. | Failures introduced by prompts, data sources, tools, interface design, or actual user practices. |
| Red-teaming | Adversarial inputs and misuse attempts, including efforts to bypass safeguards or trigger harmful behavior. | Whether the system performs acceptably in ordinary operations or in the full range of real deployment conditions. |
| Field or context-aware testing | Behavior in the intended setting, including how components, users, and operating conditions interact. | It may not cover every rare or deliberately adversarial case unless those are included in the evaluation plan. |
Use the table to identify coverage gaps, not to choose a single “best” test. For instance, a model may pass a fixed set of benign prompts while a connected tool introduces a new route to sensitive data. Conversely, adversarial testing alone does not establish that everyday use is reliable. Select tests according to the risks mapped earlier and evaluate the integrated system wherever those risks depend on integration or context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Make and document a launch decision
NIST’s Generative AI Profile states the core deployment test directly: “The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits.” Treat that as a contextual decision principle, not as a universal numeric score or a guarantee obtained by passing a benchmark.
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The launch record should make the rationale reviewable. Include the evidence considered, material limitations and unresolved failures, mitigations in place, residual risks, the person authorized to accept them, and any conditions on deployment. If a critical risk is outside the organization’s tolerance or the system cannot fail safely in the relevant conditions, delay or narrow deployment until the issue is addressed. A restricted rollout or added human checkpoint may reduce exposure, but only if that mitigation is actually implemented and evaluated.
7. Prepare monitoring, incident response, and reevaluation
Safety review continues after release because models, prompts, connected tools, user populations, data, and operating conditions can change. NIST’s profile calls for regular safety evaluation and for monitoring outputs and performance, with processes to handle detected errors and anomalies. Before deployment, establish:
- Monitoring: what outputs, performance signals, or anomalies will be checked, by whom, and how often.
- Escalation: how users and staff report harmful behavior, who triages it, and who can pause or restrict the system.
- Response and recovery: steps to contain impact, correct or roll back a change, and repair affected workflows where possible.
- Reevaluation triggers: model or prompt changes, new tools or data, new user groups or use cases, incidents, or meaningful shifts in operating conditions.
Preserve enough deployment and evaluation records to determine what changed and whether the original risk decision still applies. Reopen the assessment when those changes alter the system boundary or the people and conditions exposed to it.
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