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AI risk is the possibility that an AI system causes harm or fails in its real-world context—not just that its model makes an inaccurate prediction or is vulnerable to a cyberattack. Managing it means identifying who and what could be affected, assessing likely consequences, assigning responsibility, and monitoring the system throughout its lifecycle. Frameworks such as NIST’s AI Risk Management Framework can organize that work, but following a framework does not guarantee safe outcomes or, by itself, satisfy every applicable law.
What counts as AI risk?
AI risk includes potential effects on individuals, organizations, society, and the environment. The relevant risks depend on what a system is designed to do, how it is built and deployed, who relies on it, and what happens when its output enters a larger process. An AI tool that drafts internal meeting notes presents a different risk profile from one used to screen job applicants or support a medical decision.
NIST groups qualities associated with trustworthy AI into several connected areas: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness, with harmful bias managed. These are useful lenses for asking what could go wrong, not a universal checklist that makes every system trustworthy. See the NIST AI Risk Management Framework overview and its AI RMF FAQs.
Examples of risks to consider
- Unreliable or invalid outputs: a system may give incorrect, inconsistent, or poorly calibrated results, or fail when conditions differ from those in development.
- Safety failures: an output or action may contribute to physical, psychological, or other harm, especially where people or infrastructure depend on the system.
- Security and resilience problems: a system may be compromised, manipulated, or disrupted, or may fail to recover appropriately.
- Privacy exposure: personal or sensitive information may be collected, inferred, disclosed, or retained in ways that create harm.
- Unfair or discriminatory effects: design choices, data, or deployment practices may disadvantage particular people or groups.
- Opacity and weak accountability: people may be unable to understand a consequential decision, challenge it, or identify who is responsible for the system’s use.
- Wider societal or environmental effects: consequences may extend beyond the immediate user or organization, depending on how a system is deployed and scaled.
These risks can overlap. A flawed output may become more harmful when users over-trust it, when there is no human review, or when affected people cannot appeal. Risk assessment should therefore consider the full use context rather than treating model accuracy as a complete measure of safety.
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How to assess risk in a real use case
Risk identification asks what could happen; risk evaluation considers how serious and plausible those outcomes are in context; risk response determines what the organization will do. The following questions provide a practical starting point, rather than a mandatory checklist prescribed verbatim by NIST.
- Define the intended use. What task is the system meant to perform, and what uses are outside its intended scope?
- Map the people and processes involved. Who builds, supplies, deploys, operates, relies on, or is affected by the system? What downstream decisions or services use its output?
- Trace the system’s inputs and dependencies. Consider data, models, interfaces, human decisions, and connected processes. Identify where errors, misuse, or unexpected conditions could enter.
- Describe plausible harms. Ask how failure, bias, privacy exposure, security compromise, or over-reliance could affect people, the organization, or others.
- Evaluate severity and likelihood. Judge outcomes in the particular setting; do not assume that a single score or threshold applies across sectors or uses.
- Choose and assign responses. Decide what to prevent, mitigate, monitor, or otherwise address, and name the people responsible for each action.
- Keep evidence and reassess. Record decisions and supporting evidence, then revisit the assessment when the system, data, use, affected population, or operating environment changes.
High-impact settings—including employment, healthcare, finance, education, critical infrastructure, and public services—call for particularly careful context-specific analysis. The general frameworks discussed here do not establish a universal risk threshold or substitute for reviewing the requirements that apply to a particular system.
What the NIST AI Risk Management Framework does
NIST released AI RMF 1.0 on January 26, 2023, after a consensus-driven process. It is voluntary, non-sector-specific guidance intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. It is not a safety guarantee, a universal legal compliance certificate, or a fixed sequence of steps that removes risk. NIST describes the framework and its purpose on its AI Risk Management Framework page.
The framework’s Core has four functions: Govern, Map, Measure, and Manage. They are better understood as connected parts of ongoing risk management than as a one-time project checklist. Governance cuts across the work, and organizations need not perform the functions in a fixed order. NIST’s AI RMF Core describes their relationship and lifecycle emphasis.
Rank #3
| Function | Purpose | Practical question |
|---|---|---|
| Govern | Establish organizational context, oversight, and responsibility for AI risk management. | Who owns the system and its risks, and how are decisions and accountability maintained? |
| Map | Understand the system’s context, intended use, affected parties, and risks. | What is the system used for, and who or what could be affected? |
| Measure | Assess and analyze risks using appropriate evidence and methods. | How will the organization evaluate performance, harms, and uncertainty in this setting? |
| Manage | Prioritize and respond to identified risks. | What actions will reduce or otherwise address risk, and how will they be tracked? |
NIST’s Playbook offers suggested actions and documentation practices, while profiles address particular technologies, uses, or sectors. Those materials can help an organization adapt the framework, but they do not turn voluntary guidance into a guarantee. NIST’s AI RMF Playbook and profiles are available through its AI Resource Center.
How NIST guidance relates to ISO/IEC 23894
ISO/IEC 23894:2023 is an international standard titled “Information technology — Artificial intelligence — Guidance on risk management.” Its first edition was published in February 2023. ISO says it helps organizations integrate AI risk management into AI-related activities and functions, describes processes for implementation, and can be customized to organizational context. The standard is available for purchase from ISO; that fact does not establish availability through any other retailer. See the ISO/IEC 23894:2023 page.
NIST’s AI RMF and ISO/IEC 23894 can both inform organizational risk work, but they are not interchangeable badges or proof of compliance. When choosing or combining guidance, consider its purpose, structure, jurisdictional force, evidence expectations, fit with the system lifecycle, and implementation effort. NIST publishes information about standards alignment and crosswalks, including one related to ISO/IEC 23894, on its AI Standards page.
| Question | NIST AI RMF 1.0 | ISO/IEC 23894:2023 |
|---|---|---|
| What is it? | Voluntary, adaptable AI risk management framework. | AI-specific international guidance on risk management. |
| Who can use it? | Organizations across sectors; it is non-sector-specific. | Organizations developing, producing, deploying, or using AI products, systems, and services. |
| Does using it itself certify compliance? | No; it is not a universal legal compliance certificate. | No certification scheme is established by the standard’s guidance alone. |
| What should an organization check? | Fit with its use case, applicable law, and any relevant profiles or implementation needs. | Fit with its context and whether the guidance meets its operational and jurisdictional needs. |
Is AI risk management legally required?
There is no single answer for every organization or AI system. Legal obligations depend on jurisdiction, the organization’s role, the system’s purpose and classification, and the law in force. The general NIST and ISO sources described here do not determine which laws apply to a particular case. Do not assume that NIST AI RMF use is legally required, or that following ISO/IEC 23894 makes a system compliant. For a decision with legal consequences, check the relevant law and regulator’s current guidance for the specific jurisdiction and use.
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What is changing in the NIST framework?
NIST reports that AI RMF 1.0 is being revised. Its AI Resource Center also reports that a critical-infrastructure profile concept note was released on April 7, 2026. A concept note is not final operational requirements. Because revision and profile status can change, consult NIST’s live framework page and AI Resource Center when making implementation decisions.
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