For workplace data and IT teams, trusting AI means managing whether a system is dependable, secure, fair, understandable, and appropriate for its intended use—not accepting a vendor’s assurance as a guarantee. The National Institute of Standards and Technology (NIST) describes several characteristics of trustworthy AI and offers a voluntary framework for assessing risks throughout an AI system’s lifecycle.
AI trust is a set of qualities, not a label
NIST’s AI Risk Management Framework (AI RMF) identifies these characteristics of trustworthy AI:
- Validity and reliability: The system performs as intended and produces dependable results for its stated purpose.
- Safety: The system avoids or limits harm to people and property.
- Security and resilience: The system withstands attacks, failures, and disruptions, and can recover appropriately.
- Accountability and transparency: People can identify who is responsible and understand relevant information about the system and its use.
- Explainability and interpretability: People can understand how the system reaches or presents results to a degree appropriate for the use.
- Privacy enhancement: The system and its use protect individuals’ privacy.
- Fairness, with harmful bias managed: The system’s effects are assessed and addressed so that harmful disparities are not ignored.
These characteristics are related, but they are not interchangeable. A system may be reliable for a narrow task yet expose sensitive information, or provide understandable outputs while performing poorly for a group of users. NIST says teams should consider the characteristics across pre-design, design and development, deployment, use, and testing and evaluation, and balance them for the system’s context. See the NIST AI Risk Management Framework and its AI RMF FAQs.
Trust is therefore not an automatic certification, a binary status, or a single claim from an AI provider. It is a continuing judgment about a particular system, task, data, users, and consequences.
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Start with the workplace use and its data
Before choosing a tool or setting rules for it, describe what employees will use it to do and what data will pass through it. Include information sent to the service, information returned in outputs, and any data used to configure, evaluate, or improve the system. Consider both intended use and foreseeable misuse.
- Data and confidentiality: Identify personal, customer, employee, financial, confidential, and intellectual-property data involved. Determine what is sent to the system, where it is processed, who can access it, and whether it could be exposed through an endpoint or an output. NIST identifies potential exfiltration of training data or intellectual property through AI endpoints as a security concern.
- Integrity and reliability: Establish whether inputs and outputs can be trusted for the task. Ask what happens if an answer is incorrect, incomplete, manipulated, or out of date, and test likely failure modes rather than relying only on demonstrations.
- Availability and resilience: Consider what work depends on the service, the impact of an outage, and how the team would continue or recover. Availability matters alongside the confidentiality and integrity of the system and its data.
- People and impact: Identify who may rely on the output or be affected by it, including employees, customers, applicants, or other groups. Assess consequences of errors and whether outcomes may differ across affected groups.
- Human decisions: Clarify whether AI provides suggestions, drafts, rankings, or decisions, and who reviews or acts on the result. The more consequential the use, the more important it is to define accountability and oversight.
NIST’s security guidance covers more than the model itself: supporting software and hardware, data, and the wider system also matter. It highlights concerns such as adversarial examples and data poisoning, which can undermine system behavior. Review the AI RMF and its cybersecurity considerations when defining the scope of an assessment.
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Use NIST’s four functions to organize risk management
NIST organizes AI RMF 1.0 around four functions: Govern, Map, Measure, and Manage. They provide a structure for organizing work, not a universal checklist that determines which risks matter most for every organization. The companion AI RMF Playbook suggests actions and references for pursuing the framework’s outcomes.
Govern: assign responsibility and rules
Set out who owns the use case, who approves it, who maintains the system, and who responds to incidents or complaints. Establish policies for acceptable use, data handling, access, review, and escalation. Governance should make clear who can authorize a new use or change, rather than leaving responsibility divided between the provider, IT, and business teams.
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Map: describe context and potential impact
Document the task, system components, data flows, users, affected people, dependencies, and expected benefits. Record what decisions the system influences and what could happen if it fails, is unavailable, or produces a harmful result. This is where a team distinguishes a low-impact drafting aid from a tool whose output shapes a consequential decision.
Measure: evaluate the risks
Test performance against the intended task and investigate failure modes relevant to the context. Assess data exposure, security, reliability, fairness, and whether people can understand the system’s role. NIST’s AI Resource Center provides materials supporting operationalization, including testing, evaluation, verification, and validation resources: NIST AI Resource Center.
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Manage: choose and monitor responses
Decide whether to proceed, restrict, change, or reject a use based on the assessment. Select controls that address the identified risks, assign owners, and monitor the system and its use after deployment. Reassess when the model, data, provider, workflow, or consequences change; procurement approval alone cannot establish that a system remains suitable.
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The right controls depend on the use case, data, organizational responsibilities, and applicable obligations. NIST’s framework is voluntary and use-case agnostic; it does not prescribe one universal control set. Teams can translate the assessment into operational questions such as:
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- What information may employees enter, and what information must not be entered?
- Which identities and roles can use the system, administer it, or access its data?
- What provider, application, software, hardware, and service dependencies support it?
- How will users distinguish AI-generated content from reviewed or authoritative information?
- Which outputs require verification, and who is accountable for that verification?
- How can users report errors, security concerns, or harmful outcomes?
- What response is available if the service is compromised, produces unreliable results, or becomes unavailable?
- What changes or events trigger a fresh review?
These are decision points rather than claims that any single safeguard guarantees trust. For example, limiting input data may reduce exposure but can also constrain usefulness; human review may catch some errors but does not eliminate the need to evaluate system performance. Teams should record the trade-offs they accept and why they are proportionate to the use.
For generative AI, use the dedicated NIST profile
NIST published its Generative AI Profile (NIST AI 600-1) on July 26, 2024. It is a cross-sector companion to AI RMF 1.0 that identifies risks novel to or amplified by generative AI and suggests risk-management actions. It is relevant when evaluating generative AI systems such as large language models, including cloud services and acquired tools.
Use the profile to inform the risk review, not as proof that a particular product is safe or as a substitute for examining the specific service, data flows, and workflow. NIST states that AI RMF 1.0 is being revised; its current status should be checked on the NIST AI RMF page when applying the framework.
Keep legal and organizational obligations in view
The AI RMF is voluntary, not a legal requirement or certification. Applicable laws, sector rules, contracts, and internal policies depend on jurisdiction, industry, data, and use case. An organization should determine those obligations separately rather than treating alignment with NIST guidance as a legal compliance determination.
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The framework is useful because it gives data owners, security teams, managers, and system owners a shared way to surface and manage risks. Its value comes from applying that structure to a clearly defined use, revisiting decisions as conditions change, and making accountability explicit.
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