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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBuild a register of AI use cases across your organization, document each system’s purpose and context, and assign an owner to assess and act on its risks. An inventory helps you gather facts for governance and legal review; it does not, by itself, establish compliance. As of 7 October 2026, specified EU AI Act requirements have begun to apply, while key high-risk-system dates are later and depend on classification.
What should an AI inventory include?
Describe each use case, not just the vendor, product, or model. The same tool can create different risks depending on what it is used for, whose data it handles, who is affected, and whether its output influences a consequential decision. NIST’s AI Risk Management Framework (AI RMF) addresses risk across AI actors and the system lifecycle; it does not prescribe one universal inventory form. The fields below are a practical way to gather context for review, informed by the NIST AI RMF and its voluntary Playbook.
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- Identity and accountability: system or service name, provider, business owner, technical contact, and relevant third-party dependencies.
- Purpose and people: intended purpose, workflow, intended users, people affected, and whether the system influences decisions about people.
- Data and operation: data categories and sources, inputs and outputs, downstream decisions or systems, and human review or override.
- Deployment context: where it is used, countries involved, lifecycle status (such as pilot or production), and changes to model, configuration, or purpose.
- Risk and evidence: known limitations, relevant incidents and contact route, laws or frameworks considered, and supporting documents such as assessments, test results, contracts, or instructions.
Mark missing information as unknown rather than guessing. Give each unknown an owner and due date; otherwise, gaps can disappear into a spreadsheet without being resolved.
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How do I find AI tools employees are already using?
Use more than one discovery route. Procurement records and IT inventories may reveal approved software, but they can miss AI features embedded in existing products, employee-selected services, pilots, and tools accessed through an API. Ask teams about the work they do and the systems involved, not only whether they “use AI.”
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- Set the boundary. Include AI the organization develops, buys, configures, or uses. Include embedded features and generative AI services, not only products marketed as AI platforms.
- Ask across functions. Have business units, procurement, IT, security, legal, and data teams identify software, models, APIs, pilots, and known unsanctioned uses. Compare their responses with purchasing, access, and system records available to the organization.
- Describe the actual workflow. For each lead, ask what task the system performs, who uses its output, who may be affected, what data goes in, and what happens next. A product name alone cannot answer those questions.
- Assign two contacts. Name a business owner who can explain the purpose and a technical contact who can explain the system and its dependencies. Route unresolved leads to an accountable owner.
This is an organizational discovery method, not an official NIST template. NIST’s AI RMF Playbook offers suggested actions and documentation practices for using the framework, but organizations choose how to operationalize them.
How do I assess AI risk?
Start by identifying issues that need immediate review before assigning a numerical score. Check for potentially prohibited or restricted uses, impacts on safety or rights, sensitive data, consequential decisions, exposure of minors or other vulnerable groups, cybersecurity concerns, and reliance on third-party providers. Record what is known, what remains uncertain, and who must investigate.
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Then organize assessment work around NIST’s four AI RMF functions: Govern establishes accountability and risk practices; Map documents context and potential impacts; Measure evaluates risks with appropriate methods; and Manage prioritizes and responds to them. These are framework functions, not a mandated scoring formula. NIST describes the AI RMF as intended for voluntary use to improve the incorporation of trustworthiness considerations into AI design, development, use, and evaluation. See the framework and its Playbook.
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Turn findings into decisions and follow-up
For each use case, record the risk rationale and decision, evidence reviewed, mitigation or further test required, accountable owner, and review trigger. A practical prioritization can consider severity to affected people and the organization, likelihood or exposure, scale, reversibility, detectability, uncertainty, and legal urgency. These are useful decision factors, not a scoring scale required by NIST.
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- 🔥Setting Keypad & LCD Display & Al Voice: Compared with traditional barcode scanners on the market, ❶Customizable Menu - Setting keypad helps you to set up various functions (No more need to scan the complex barcodes for settings). ❷1.5" LCD Screen to display the Setting, Wireless, Bluetooth, Battery, Scanning and Storage Status for easier and more intuitive operation. ❸Say goodbye to the buzzer, AI Voice will prompt the usage status like friend.
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- If a material fact is unknown, assign the work needed to establish it rather than treating the uncertainty as low risk.
- If a mitigation is required, state what will change, who will implement it, and what evidence will show whether it worked.
- If the organization accepts or escalates a risk, record who made that decision and under what authority.
Which AI systems are high-risk under the EU AI Act?
Do not infer legal classification from a product label, a vendor’s marketing, or an internal risk score. For an EU-related use, examine the Act’s definitions, prohibited practices, and high-risk classifications against the system’s intended purpose and actual context. 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. The binding text is the consolidated EU AI Act; assess the particular system and the organization’s role under the law rather than treating the guidance as a final legal ruling.
For systems classified as high-risk, the Commission summarizes requirements that include risk assessment and mitigation, data quality, logging, technical documentation, information for deployers, human oversight, robustness, cybersecurity, and accuracy. Some covered public-service and other deployers must conduct a fundamental rights impact assessment before deployment; this is not a blanket requirement for every AI use. Consult the Commission’s AI Act regulatory framework overview and the Act for the applicable duties.
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What are the EU AI Act deadlines?
The Act’s timetable is staged. As of 7 October 2026, the Commission says enforcement powers and applicable requirements for prohibited practices, transparency, and general-purpose AI began on 2 August 2026. The Commission’s high-risk guidance page reports a revised timetable following the political agreement on the AI Omnibus. The relevant high-risk dates shown in the Commission guidance and consolidated Act are:
| Application date | Scope described in the sources |
|---|---|
| 2 August 2026 | Enforcement powers and applicable requirements for prohibited practices, transparency, and general-purpose AI, according to the Commission AI Act Service Desk’s enforcement-start FAQ. |
| 2 December 2027 | High-risk systems under Article 6(2) and Annex III, according to the Commission’s high-risk guidance page and the consolidated Act. |
| 2 August 2028 | High-risk AI embedded in regulated products under Article 6(1) and Annex I, according to the Commission’s high-risk guidance page and the consolidated Act. |
These dates do not mean every organization or AI system is subject to the same duties on the same day. The Act’s scope and requirements depend on the system, use, and organizational role; the consolidated text and Commission pages can change. Check the live legal text and current Commission information before relying on a date for a specific deployment.
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Does the NIST AI RMF make us compliant?
No. The NIST AI RMF is voluntary, while the EU AI Act is binding within its scope. Using NIST’s Govern, Map, Measure, and Manage functions can structure risk work and documentation, but it does not determine whether a system falls under a law or satisfy every legal duty. An inventory is an input to classification and review, not proof of compliance.
Legal obligations also vary by geography, sector, system, and the organization’s role. The sources here do not provide a complete account of current U.S. federal, state, sector-specific, or other national requirements. Identify the relevant countries, sector, use case, and role, then check current official sources or consult qualified counsel for jurisdiction-specific advice.
How do I keep the inventory current?
Set a review cadence appropriate to the organization and define event-based triggers so material changes are not left for the next scheduled review. Reopen an entry when there is a new use case, model or vendor change, new data, changed purpose or affected population, material incident, deployment in another country, or change in applicable law. Assign responsibility for monitoring the relevant systems and regulatory sources; NIST describes its AI RMF as a living framework, so use the current NIST framework page alongside applicable local authority guidance.
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