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Build one maintained record of the AI systems and tools your company develops, buys, pilots, or uses—and link each entry to its owner and relevant supporting records. That inventory helps auditors and internal teams understand what exists; it does not, on its own, prove a system is safe, that controls work, or that every legal obligation is met.
What an AI inventory is—and what it is not
NIST defines an AI system inventory as “an organized database of artifacts relating to an AI system or model.” In practice, that means more than a list of product names: each entry should identify a system or meaningful use and point to records that help people understand and maintain it. See the NIST AI RMF Playbook, Govern.
An inventory is one mechanism in AI risk governance. It helps answer system-level questions, such as when a model was last refreshed, and portfolio-level questions, such as how many models are deployed or how many users are affected. It can support maintenance and incident response, but it is not a safety assessment, control test, legal certification, or complete audit checklist.
How to build the inventory before an audit
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Define and document the boundary
Decide what your company counts as an AI system or tool, and specify which legal entities, business functions, pilots, production deployments, and third-party services are in scope. Record exclusions and why they were excluded. NIST recommends defining which systems are included; its preference is broad coverage, with high-risk systems or systems in high-stakes settings prioritized at minimum when full coverage is not feasible. See Govern 1.6 in the NIST AI RMF Playbook and the NIST AI RMF Core.
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Discover tools across teams and records
Ask business, product, engineering, data, security, procurement, legal, and operations teams what they build, buy, test, or use. Cross-check their responses against existing system and vendor registers, model documentation, implementation references, and incident records. This is a practical discovery approach, not a checklist prescribed by NIST; it is intended to help make the inventory boundary real across the company.
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Create one useful record per system or meaningful use
Use a stable name and describe the system or use in terms colleagues can recognize. A practical record can capture:
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- Purpose and business process.
- Status, such as proposed, pilot, production, or retired.
- Internal owner and relevant AI actors, with contact details.
- Provider or implementation reference, where applicable.
- Links to system documentation, data dictionaries, implementation software or source code, and incident response plans, where available and appropriate.
- Last-reviewed or model-refreshed date, if known.
NIST gives documentation, incident response plans, data dictionaries, implementation links, and relevant actors’ names and contact information as examples of inventory artifacts. Its examples should be adapted to the organization; NIST does not mandate this exact field set.
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Name the inventory maintainer
Assign a specific person or team to maintain the inventory. State how system changes—such as a new deployment, a change of owner, or retirement—are reported and reviewed. NIST recommends defining both responsibility for maintenance and the inventory’s scope and attributes.
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Triage for deeper review without losing visibility
Keep the broad inventory distinct from decisions about which systems need more risk review. Use your organization’s risk priorities and high-stakes contexts to identify where deeper review is needed. If full coverage cannot be achieved immediately, make the unrecorded scope visible and prioritize high-risk systems rather than implying that the partial inventory is complete.
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Check whether entries can be followed
Choose representative entries and ask their owners to locate the linked records and answer relevant questions about the system. Track missing owners, unclear scope, absent evidence links, and stale review dates as gaps with an owner and next action. This is a practical audit-preparation check inferred from the inventory’s intended records and uses, not a NIST-prescribed audit test.
Choose a format that supports coverage and upkeep
NIST does not rank inventory products or prescribe a storage format. A spreadsheet, an existing asset register, or a dedicated workflow platform may work if it fits the organization’s needs. Compare options on whether they can:
- Capture in-scope systems, including pilots and third-party tools.
- Show a responsible owner and relevant contacts for each entry.
- Link to supporting documentation and implementation or incident records.
- Be updated as systems, owners, and statuses change.
- Answer portfolio-wide questions, not just display one record at a time.
The right format is the one your teams can keep current and reviewers can navigate. A tool choice does not itself establish compliance.
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Keep inventory, risk review, and assurance separate
These activities are related, but they answer different questions:
- Inventory: What AI systems and tools are in scope, who is responsible for them, and where are the relevant records?
- Risk review: Which systems warrant closer assessment under the organization’s priorities and the context in which they are used?
- Assurance: What evidence shows that applicable controls operate and relevant obligations are met?
A complete inventory can make risk review and evidence gathering more manageable, but cannot substitute for either. The NIST AI Risk Management Framework is voluntary guidance, not a universal legal audit checklist. NIST says AI RMF 1.0 was released on January 26, 2023 and is being revised; the Playbook is to be updated after the framework revision. See NIST’s AI RMF Playbook page and the NIST AI Resource Center. Which records, disclosures, or controls are legally required depends on jurisdiction, sector, system use, and audit scope.
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