Assess an AI tool against the job you plan to give it—not a vendor’s general safety claims or a single benchmark. Before adoption, define who will use it, who may be affected, what data and decisions are involved, and what a failure could cause. Then review evidence, test realistic tasks and failure cases, set safeguards, and decide how the system will be monitored or stopped.
Start with the proposed use, not the product label
The same AI system can carry different risks in different settings. A tool used to draft internal notes is not equivalent to one whose output influences hiring, access to services, health, safety, or other consequential decisions. The OECD recommends scoping risks and prioritizing them in light of each organization’s circumstances in its Due Diligence Guidance for Responsible AI.
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Write down the proposed deployment before comparing products:
- Task: What will the system do, and what is outside its remit?
- Users and affected people: Who operates it, relies on its output, or may be affected by it?
- Data: What information will it receive, including personal or confidential data?
- Output and authority: Will it suggest, rank, decide, generate content, or take actions? What human supervision remains?
- Failure consequences: What could happen if an output is wrong, misleading, biased, insecure, or unavailable?
This scope makes it possible to judge whether evidence and safeguards fit the actual use rather than a generic product description.
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Map the harms and trustworthiness dimensions
Consider who benefits, who could be harmed, and how. Potential effects may involve privacy, security, fairness, safety, work, access to services, or important decisions. Risks can connect: misuse or repurposing, unreliable or biased outputs, exposure of sensitive information, security failures, and unclear accountability may reinforce one another. The OECD notes that AI risks can overlap and that dual-use capabilities may enable harmful uses even when a system was intended for benign purposes.
Do not reduce the review to a single “safe or unsafe” score. NIST identifies several trustworthiness characteristics, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness. Their relative importance depends on the setting, and tradeoffs can arise. The NIST AI Risk Management Framework FAQ cautions that addressing characteristics separately does not by itself ensure trustworthiness; not every characteristic applies equally in every setting.
Request evidence that matches your deployment
Ask the provider for material that relates to your intended task, data, users, and operating conditions. The cited frameworks do not prescribe one universal vendor questionnaire; use these prompts to guide due diligence, not as a claim that a standard checklist has been satisfied.
- What uses is the tool intended for, what uses are excluded, and what limitations are known?
- How was it evaluated, and do results cover relevant users, conditions, and failure cases?
- How are data collected, processed, stored, retained, and protected? Can your organization control what information is submitted?
- What security and resilience measures, incident-response procedures, and update practices apply?
- What human oversight or operational controls are available, and how can users report or correct a problem?
- Which claims can be independently verified, and which rely only on provider statements?
Request results and documentation in enough detail to judge relevance. A broad claim such as “tested for safety” is a starting point for questions, not proof that a tool is suitable for your use. NIST’s AI Risk Management Framework addresses risk management across design, development, use, and evaluation; OECD recommends deeper due diligence when risk indicators warrant it.
Test realistic tasks and foreseeable failures
Test the candidate in conditions that resemble the planned deployment. Build cases from real tasks, edge cases, foreseeable misuse, and failure modes that matter to affected people. Assess not only whether outputs are useful, but also whether errors can be detected and whether safeguards work when the system is uncertain or wrong.
- Set the test scope: Use representative inputs, relevant user groups and operating conditions, and the data handling planned for deployment.
- Include difficult cases: Probe ambiguity, unusual inputs, missing information, sensitive data, and attempts to use the system outside its intended purpose.
- Define pass criteria in advance: Set thresholds and review rules based on the consequences of errors in this use case; an aggregate score alone may conceal important failures.
- Record what happens: Note failures, safeguards triggered, human interventions required, and unresolved uncertainty.
There is no universal test suite in the cited guidance. NIST’s framework supports lifecycle risk management, but the test cases and acceptable thresholds must fit the deployment.
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Choose safeguards and escalation rules before rollout
Match controls to the risks you identified, and decide in advance when a concern requires deeper review or blocks adoption. OECD guidance suggests an escalation system when it is impractical to conduct in-depth assessments of every AI system.
- Restrict access, data types, or uses where needed.
- Require human review for outputs or actions whose errors could have serious consequences.
- Train users on appropriate use, limitations, and how to report problems; disclose AI involvement where appropriate.
- Monitor performance and incidents in operation, with an owner responsible for reviewing signals and taking action.
- Define a pause, rollback, or removal path if the tool behaves unexpectedly or controls fail.
- Set escalation triggers, such as a change in intended use, a serious incident, evidence of unequal impact, or an unresolved security or privacy concern.
Compare candidates on the dimensions that matter
Use a side-by-side review tied to the defined use. These are comparison axes, not a universal weighted scorecard: the importance of each depends on the context.
| Dimension | What to compare |
|---|---|
| Task performance and reliability | Results on your intended task, relevant users, and operating conditions; failure detection and known limitations. |
| Potential harms | Likelihood and severity of harms in the deployment, including effects on people beyond the direct user. |
| Data, privacy, security, and resilience | Data handling and protections, security practices, and the ability to operate safely through failures or disruption. |
| Transparency and accountability | Whether people can understand relevant outputs, identify responsibility, challenge or correct them, and obtain explanations appropriate to the use. |
| Human and operational controls | Available oversight, access restrictions, incident response, monitoring, and pause or rollback options. |
| Evidence quality | Whether documentation and evaluations are relevant to your deployment, independently verifiable, and candid about limitations. |
| Legal and regulatory fit | Whether the specific use and geography bring applicable obligations or restrictions. |
The dimensions reflect NIST’s trustworthiness characteristics and the OECD’s context-based risk prioritization. Do not let a strong result on one dimension conceal a critical weakness on another.
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Check applicable rules and reassess when circumstances change
Ask qualified compliance or legal staff to check the rules that apply in each relevant jurisdiction. For use in the European Union, consult current AI Act materials and establish whether the specific use falls within a high-risk category. The European Commission describes high-risk use cases as those that can pose serious risks to health, safety, or fundamental rights in its AI Act overview.
The Commission’s high-risk systems guidance page describes draft, nonbinding guidance pending formal adoption. Do not treat that draft as a definitive legal classification: confirm current law and scope for the specific use.
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Use frameworks as guides, not safety certificates
NIST describes AI RMF 1.0 as voluntary guidance and says the framework is being revised. Its resources page lists the framework, playbook, and other materials. These resources support structured risk management; they are not certification or a guarantee that a particular system is safe.
For generative AI, NIST AI 600-1 is a companion to AI RMF 1.0. Released on July 26, 2024, the Generative AI Profile describes risks that are novel to or worsened by generative AI and suggests management actions across the lifecycle. Use it alongside, rather than instead of, an assessment of the specific deployment.
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