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Start with a defined use case and accountable owners
Write down the job the assistant is meant to perform before selecting settings or connecting it to company systems. A clear scope gives you something concrete to test and a basis for deciding whether the benefits justify the risks.
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- Purpose and users: What task should it perform, who will use it, and in what operating environment?
- Boundaries: What requests or actions are out of scope? What outcomes would be unacceptable?
- Success and risk tolerance: What result would make the deployment useful, and what level of error or harm is unacceptable?
- Accountability: Who can approve, restrict, or stop the deployment, and who handles its day-to-day operation?
Involve the functions affected by the system—such as product or engineering, security, privacy, legal, compliance, and business operations. NIST’s AI Risk Management Framework organizes suggested work under four functions: Govern, Map, Measure, and Manage. Organizations can select and adapt the guidance to their use case; the framework is voluntary. NIST’s AI RMF Playbook provides suggested actions, while the AI RMF overview describes the framework and its status.
Map the people, data, tools, and possible harms
Trace what happens from a user’s prompt to the assistant’s response and any action that follows. Include the model and service provider, retrieval sources, APIs or plugins, and downstream systems. The map should show what information moves where, who can access it, and what the assistant can change or communicate.
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Consider more than incorrect answers. A deployment may create confidentiality, privacy, security, availability, intellectual-property, or harmful-bias risks. Users may also over-rely on confident but misleading output, or someone may try to misuse the system. Identify who could be affected, including people who never interact with the assistant directly.
Pay particular attention to permissions. An assistant that only drafts text presents a different exposure from one that can retrieve sensitive records, modify files, trigger transactions, or send messages externally. As access and autonomy increase, consider narrower permissions, separation of duties, human approval for consequential actions, and a fallback that still works if the assistant is unavailable or unsafe. These are risk-based safeguards, not controls NIST requires in every deployment.
Test the system you intend to deploy
A polished demo or a strong benchmark score does not establish that an assistant will behave reliably in your organization’s workflows. Build tests around the actual task, users, data, connected tools, and consequences of failure. NIST’s 2024 Generative AI Profile cautions that pre-deployment evaluations may not fit the deployment context; standardized tests, demonstrations, and jailbreak tests alone cannot establish validity or reliability for a particular domain.
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Include ordinary use and failure cases
Test typical requests as well as situations where the system should be careful, refuse, or defer. Include ambiguous questions, out-of-scope requests, missing information, sensitive-data handling, adversarial or manipulative inputs, and unavailable or failing connected services. If tools are connected, test their permissions and confirm that the assistant cannot perform actions outside its intended authority.
Record results and retest meaningful changes
Document the test conditions, what passed or failed, known limitations, and risks that remain. Use representative users or reviewers where appropriate. Re-run relevant tests when the model, prompts, data sources, tools, user population, or deployment context changes.
“Robust test, evaluation, validation, and verification (TEVV) processes can be iteratively applied – and documented – in early stages of the AI lifecycle and informed by representative AI Actors.”
— National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (2024), p. 49. Read the NIST Generative AI Profile.
Set human oversight and clear user expectations
Decide which responses the assistant can provide on its own, which outputs need review, and when it must defer to a person. Give reviewers enough context to assess an output rather than treating approval as a formality. Define how users can challenge a result, correct an error, or escalate a problem.
Explain the assistant’s intended role and limitations to users in terms they can act on. The right level of review depends on the likely consequences of error, how users may interpret the output, and whether a reviewer can meaningfully understand and challenge it. Higher-impact uses generally warrant tighter oversight; there is no single NIST threshold that sets the required level for every case. NIST notes that generative AI may call for additional review, tracking, documentation, or management oversight depending on the context.
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Set rules for what employees and other users may enter. Address confidential, personal, regulated, and proprietary information explicitly instead of assuming users will know what is safe to share.
For each external provider or connected service, review the terms and technical practices relevant to your deployment. Clarify how prompts and outputs are collected, used, retained, and accessed; what security controls apply; and how and when the provider will notify you about an incident. Confirm who is responsible for investigating, communicating, and responding when an issue involves a supplier.
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NIST recommends updating procurement and acquisition due diligence to consider intellectual-property, privacy, security, and other risks. Depending on the service and your needs, mechanisms such as software bills of materials, service-level agreements, or assurance reports may help with transparency and third-party risk management. They are options to evaluate, not artifacts every organization must obtain.
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Prepare for incidents, changes, and a safe exit
Decide before launch who can act during an incident and how quickly they can limit exposure. The response plan should identify escalation contacts and cover how to disable an integration, revoke access, switch to a fallback, preserve relevant records, and communicate with affected parties. Rehearse the plan and revise it after incidents or exercises.
Keep track of meaningful changes in the model or supplier service. Reassess risk when the purpose, configuration, data sources, permissions, user population, or surrounding systems change. Set conditions in advance for restricting operation, rolling back a change, or decommissioning the assistant.
Compare assistants by deployment risk, not by a generic ranking
When comparing products or configurations, evaluate the fit for your use case rather than treating one feature or checklist as proof that a system is safe. NIST cautions that trustworthiness characteristics can involve trade-offs, and which ones matter most depends on the setting.
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- Impact: Who could be harmed by a wrong, biased, or misleading output, and how serious could the consequences be?
- Data handling: What information is sent, stored, retained, or used by the provider, and what controls apply?
- Access and autonomy: Can it draft only, retrieve sensitive information, call tools, or take external actions?
- Oversight: Which outputs need review, and can a reviewer realistically understand and challenge them?
- Evaluation: Do the tests reflect actual users and workflows, and are limitations documented?
- Supplier resilience: What is known about dependencies, incident response, service continuity, and fallback options?
Understand what NIST guidance does—and does not—settle
NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile, NIST AI 600-1, on July 26, 2024. The current framework page says version 1.0 is being revised and records an April 7, 2026 concept note for a profile on trustworthy AI in critical infrastructure. The NIST AI RMF FAQs, updated August 13, 2026, describe the framework as a way to help developers, users, and evaluators manage AI risks that could affect individuals, organizations, society, or the environment.
The framework and profile are general risk-management guidance. They do not determine the legal duties that apply to every sector or jurisdiction, and they do not provide one universal pass/fail test or guarantee that safeguards will eliminate risk. Check applicable requirements for the location, data, decisions, and people involved in your deployment.
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