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Before choosing a model, vendor, or architecture, write down what the AI system is meant to do, who it may affect, what could go wrong, and what evidence would count as success. That brief gives a team a basis for deciding whether to proceed—and for testing the system against the actual job it is meant to perform.
NIST’s AI Risk Management Framework (AI RMF) is a useful voluntary structure for this work, not a universal definition of responsible AI or a guarantee of compliance. Released in 2023, it is currently being revised; its core ideas still offer a practical way to scope a project before implementation choices become difficult to change.
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What to write down before building an AI system
Start with the problem and the setting, not the technology. A system that performs acceptably for one task, user group, or deployment environment may be unsuitable in another. NIST emphasizes that early choices about purposes and objectives can shape a system’s behavior and capabilities.
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Use the following brief as a working document. It is a practical synthesis of NIST framework outcomes, not a required NIST form.
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- Purpose: What user or organizational need are you addressing? Name the specific task the system will support and the benefit you expect it to provide.
- Users and affected people: Who will use the system? Who may be affected by its outputs, including people who never interact with it? Identify whose expertise and lived experience should inform the requirements.
- Context and boundaries: Where and under what conditions will it operate? Record intended uses, foreseeable uses, out-of-scope uses, assumptions, user expectations, relevant laws and norms, and important knowledge limits.
- Benefits and costs: State the expected benefit and the baseline against which you will judge it, such as the current process. Include the possible costs of errors, misuse, exclusion, and failures of trustworthiness—not just financial costs.
- Requirements and tolerances: What must the system do, and what must it protect? Specify unacceptable failures and the residual risks the organization is willing to tolerate. Do not treat every error as equally consequential.
- Oversight and response: Who reviews consequential outputs? What authority and practical ability do they have to correct, override, or reject an output? Describe what should happen when the system is uncertain, unavailable, or wrong, and how it should fail safely.
- Evidence: Which tests, metrics, benchmarks, and deployment conditions would demonstrate that requirements are met? Identify who reviews the results and who makes the go/no-go decision.
- Change triggers: Which changes in data, users, use, system capabilities, or impacts require reassessment?
Requirements should be elicited from relevant people and informed by what is known about the system’s limits. That includes the people who build or operate it, domain experts, affected communities, and those responsible for decisions that depend on its outputs.
How to define success in observable terms
“It seems useful” is not an evaluation plan. Translate each important requirement into evidence that can be gathered and reviewed. NIST calls for objective, repeatable, or scalable testing, evaluation, verification, and validation processes, with metrics and methods documented. In this context, validation means obtaining objective evidence that requirements for the intended use have been fulfilled.
- Match tests to the task: Evaluate the outputs or actions the system is actually intended to support, rather than relying on a general-purpose score that may not reflect the use case.
- Test realistic conditions: Use data, workflows, users, and operating conditions that resemble the intended deployment. Record where test conditions differ from reality.
- Measure meaningful harms as well as benefits: Check for the errors, exclusions, privacy, fairness, safety, security, or transparency concerns that matter in this context. The relevant set depends on the use.
- Set decision thresholds in advance: Agree what results would permit a pilot, require changes, or stop deployment. Document who has authority to make that decision.
- Keep evidence interpretable: State what a metric measures, what it does not measure, and which users or conditions it covers. A passing result is not evidence about cases the evaluation did not examine.
Write down the organization’s tolerance for risk before reviewing results. A metric can look strong while leaving a consequential failure mode unresolved; a threshold is meaningful only in relation to the impact of being wrong and the task’s requirements.
Use the brief to decide whether to proceed
The NIST AI RMF groups its activities into Govern, Map, Measure, and Manage. Mapping the context and impacts provides a basis for an initial go/no-go decision. If a team proceeds, it should continue with measurement and risk management rather than treating the initial brief as approval for all future uses.
Ask whether the intended task, affected people, expected benefits, requirements, oversight, and evaluation approach are clear enough to make a responsible decision. If key impacts or requirements remain unknown, the next step may be to gather expertise or refine the proposal—not to start implementation.
Compare build, buy, and no-AI options against the same criteria
If there are real alternatives, compare them using the same documented context and requirements. “No AI” should remain a live option: a simpler process or existing tool may meet the need with fewer risks or less lifecycle burden.
| Comparison criterion | Question to ask |
|---|---|
| Fit to task and users | Can this option support the specified task for the people and setting involved? |
| Expected benefit | Does it improve on a suitable baseline, and how will that improvement be measured? |
| Error consequences | What happens when it is wrong, and are those consequences within the organization’s stated risk tolerance? |
| Safeguards and oversight | Can the option meet the relevant privacy, fairness, safety, security, transparency, and human-oversight needs? |
| Evidence quality | Is there credible evaluation under conditions that resemble deployment, and are its limits understood? |
| Limits and lifecycle burden | What constraints, generalizability concerns, monitoring, maintenance, and reassessment will the option require? |
Trustworthiness characteristics can involve tradeoffs, and not every characteristic matters equally in every setting. Set priorities with the relevant stakeholders instead of reducing different kinds of risk to a single universal score.
Keep the definition current after deployment
Risk management applies across pre-design, design and development, deployment, use, and testing and evaluation. The original brief should therefore be revisited as the context, capabilities, risks, benefits, and impacts change.
- Reassess when the system is used for a new purpose, by a new group, or in a different setting.
- Review the brief when data, system capabilities, workflow, or oversight arrangements change.
- Check whether observed benefits and harms match the assumptions and thresholds that supported the decision to proceed.
- Update requirements and evaluations when new evidence or affected people’s experience changes what “right” should mean.
What the NIST guidance does—and does not—establish
NIST AI RMF 1.0 was released on January 26, 2023. NIST describes it as voluntary, rights-preserving, non-sector-specific, and use-case agnostic. The framework is being revised, so it should not be presented as a finalized or mandatory standard. Its companion AI RMF Playbook offers suggested actions for Govern, Map, Measure, and Manage; NIST says the Playbook will be updated after the framework revision.
The OECD’s 2026 Due Diligence Guidance for Responsible AI notes that terms such as “define,” “identify,” “map,” and “scope” describe broadly similar scoping work across risk frameworks, even though terminology varies. The practical point is to make the context and risks explicit, whatever label a team uses.
Neither a written brief nor use of a voluntary framework proves that a system is safe, fair, accurate, or legally compliant. Applicable obligations depend on the sector, jurisdiction, and deployment. Identify them with qualified legal and domain expertise.
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Sources: NIST AI Risk Management Framework; NIST AI RMF 1.0; NIST AI RMF Playbook; OECD Due Diligence Guidance for Responsible AI.
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