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Start by recording each AI use case—not just the vendor or model—and who owns it, what it does, whose data and decisions it affects, and where it is deployed. Then triage the use case, assess likely harms and legal relevance, assign follow-up work, and set review triggers. An inventory makes that work possible; it is not, by itself, a legal classification or proof of compliance.

The practical workflow below is designed for organizations that develop, buy, configure, or use AI. The NIST AI Risk Management Framework (AI RMF) offers a voluntary structure for managing risk. The EU AI Act is binding within its scope, but its duties and dates depend on the system and the organization’s role. Other jurisdictions may impose different requirements.

How do I create an AI inventory?

Give one person or team responsibility for maintaining the register, and require a named business owner and technical contact for every entry. Include AI the organization builds, purchases, configures, or uses—including AI features embedded in ordinary software, external generative AI services, pilots, APIs, and models used behind internal tools.

There is no single official inventory template established by the sources below. The fields that follow are a practical starting point, not a prescribed NIST schema. Use a central register that can be updated, searched, and linked to supporting evidence.

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What should an AI inventory include?

  • System and provider: product or service name, provider, model or material dependencies, and whether the system is internally developed, configured, or third-party.
  • Ownership: accountable business owner, technical contact, and teams responsible for procurement, security, privacy, or compliance review.
  • Purpose and users: intended purpose, workflow, internal users, and people or groups affected by the system.
  • Data and decisions: data categories and sources, inputs and outputs, and whether outputs inform or determine decisions about people.
  • Deployment context: countries and settings where it is used, lifecycle status (proposed, pilot, active, paused, or retired), and the organization’s role in relation to the system.
  • Safeguards and limits: human review and override, known limitations, incident or escalation contact, and relevant security measures.
  • Assessment evidence: applicable laws or frameworks considered, risk decisions, test results, contracts, technical documentation, and other supporting records.

Mark unknown information explicitly instead of leaving blank cells that could be mistaken for “not applicable.” Assign each gap an owner and due date. Capture the real use and workflow: one product may support several distinct use cases with different affected people, data, or consequences, so a vendor name alone is not a useful risk record.

How do I find AI tools employees are already using?

Run discovery across business units and the teams most likely to encounter AI in normal work. Ask about software features, browser and mobile tools, APIs, model access, prototypes, vendor pilots, and workarounds. Combine those interviews with procurement and contract records, software and cloud inventories, security reviews, data-flow documentation, and privacy assessments where available.

Do not treat an approved-software list as a complete inventory. Employees may use AI features inside products already approved for other purposes, or use external services outside formal procurement. Establish a straightforward way to disclose a new use and route it for review before sensitive data or consequential decisions are involved. Discovery is ongoing: repeat it when teams, products, and workflows change.

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How do I assess AI risk?

First triage each use case; then decide which risks merit deeper assessment. A single numerical score can obscure the reason a use is concerning, so record the factors, evidence, decision, and accountable owner rather than relying on a score alone.

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1. Triage the use case

Check whether it involves sensitive data, decisions with significant effects on people, children or other potentially vulnerable groups, safety impacts, cybersecurity exposure, or reliance on third-party providers. Identify the scale of use and whether a person can meaningfully review, challenge, or reverse an output. For deployments connected to the EU, check the AI Act’s definitions, prohibited practices, and high-risk categories against the actual intended purpose and context—not just the product’s marketing or label.

2. Map potential harms and exposure

Describe what could go wrong, who could be harmed, and through which part of the workflow. Consider severity, likelihood or exposure, number of people affected, reversibility, ability to detect an error, uncertainty, and legal urgency. These are practical prioritization factors, not a scoring scale mandated by NIST. Explain the rationale for the priority you assign.

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3. Choose treatment and evidence

For material risks, determine whether to avoid or pause the use, change its design or scope, add controls and human oversight, or accept a documented residual risk through the appropriate authority. Select tests and monitoring suited to the intended purpose, data, users, and possible harms. Record the decision, evidence, responsible owner, due date, and conditions that would trigger a reassessment.

The NIST AI RMF organizes this work into four functions:

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  • Govern: establish accountability, policies, roles, and oversight for AI risk.
  • Map: understand the context, intended use, affected people, and potential harms.
  • Measure: use appropriate methods to analyze and track risks and trustworthiness characteristics.
  • Manage: prioritize risks, choose responses, and monitor them over time.

NIST describes the AI RMF as intended for voluntary use. Its AI RMF Playbook suggests actions and references for applying the framework; it is not a statutory checklist. The NIST AI Risk Management Framework is a useful organizing structure, not a substitute for determining which laws apply.

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Which AI systems are high-risk under the EU AI Act?

High-risk status depends on the Act’s definitions and the system’s use, not simply on whether a vendor calls a product “AI.” The European Commission’s page on guidelines for providers and deployers of high-risk AI systems describes its classification guidance as draft and not legally binding. Use the consolidated legal text and qualified advice where a legal determination is needed.

For systems that fall within the high-risk requirements, the Commission summarizes obligations including risk assessment and mitigation, data quality, logging, technical documentation, information to deployers, human oversight, robustness, cybersecurity, and accuracy. Some covered deployers—including certain public-service deployers—must conduct a fundamental rights impact assessment before deployment; this is not a blanket requirement for every AI use. The applicable duties depend on the system’s classification and the organization’s role, such as provider or deployer.

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What are the EU AI Act deadlines?

As of 7 October 2026, the Commission’s current timeline reflects a political agreement on the AI Omnibus. The dates below identify the stated application points for the specified requirements; they do not mean every AI obligation begins on the same day.

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Date Stated application point
2 August 2026 Enforcement powers and applicable requirements for prohibited practices, transparency, and general-purpose AI begin, according to the Commission’s AI Act Service Desk.
2 December 2027 Annex III high-risk systems: the Commission’s revised timeline and consolidated Act result identify this date for Article 6(2)/Annex III.
2 August 2028 High-risk AI embedded in regulated products: the Commission’s revised timeline and consolidated Act result identify this date for Article 6(1)/Annex I.

For the timeline, see the Commission’s “When does enforcement start?” page, its high-risk systems guidance page, and the consolidated text of Regulation (EU) 2024/1689. Dates and amendments can change. Before relying on a date for a particular system, verify the live legal text and its applicability to that system.

How do I keep the inventory useful after launch?

Set review triggers rather than waiting for an annual check. Reassess an entry when the organization adds a use case, changes a model or provider, introduces new data, changes the purpose or affected population, experiences a material incident, deploys in another country, or faces a relevant legal change. Retain prior decisions and evidence so reviewers can see what changed and why the assessment was updated.

Make the register part of existing workflows: procurement can flag proposed AI services, security and privacy teams can route new data or integrations for review, and business owners can report purpose or population changes. A risk entry becomes actionable when it has a clear owner, documented evidence, a decision, and a next review point.

Does the NIST AI RMF make us compliant?

No. NIST says the AI RMF is intended for voluntary use. It can help an organization structure risk management, but it does not itself establish compliance with the EU AI Act or another law. The EU AI Act is binding within its scope; which requirements apply depends on the system, context, jurisdiction, and organizational role.

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For obligations outside the EU, this article does not establish a complete account of U.S. federal, state, sector-specific, or other national rules. Identify where the system is developed and used, the sector, the organization’s role, and the use case; then consult current official sources or qualified counsel for jurisdiction-specific requirements. NIST’s AI Resource Center provides framework resources, but neither a framework nor an inventory replaces legal analysis.

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