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What an AI inventory is—and what it is not
NIST’s AI RMF Playbook defines an AI system inventory as “an organized database of artifacts relating to an AI system or model.” That is broader than a list of product names: it connects each system or meaningful use to information that helps people understand, maintain, and respond to it. See NIST’s Govern guidance.
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Keep three tasks distinct:
- Inventory: Identify what exists, define what is in scope, assign owners, and link relevant records.
- Risk review: Use organizational priorities and context to decide which systems need closer assessment.
- Assurance and compliance: Evaluate whether controls work and determine which legal or audit requirements apply. A completed inventory alone does not establish either.
NIST describes AI RMF Govern 1.6 as calling for mechanisms to inventory AI systems and resources in line with organizational risk priorities. The AI RMF Core is guidance, not a universal legal audit checklist.
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1. Define and record the boundary
Write down which legal entities, departments, processes, deployments, pilots, and third-party services the inventory covers. Decide what your organization means by an AI system or tool, and document exclusions with their reasons. Broad coverage is preferable; if it is not immediately achievable, NIST’s Playbook says to prioritize at least high-risk systems or systems used in high-stakes settings.
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2. Discover systems across the company
Ask business, product, engineering, data, security, procurement, legal, and operations teams what they build, buy, test, or use. Look beyond formally approved production deployments: pilots and externally provided services may be relevant to the scope you have defined. Check existing system and vendor records, model documentation, implementation references, and incident records. This cross-team process is a practical way to find entries; NIST supports broad inventory coverage and useful records but does not prescribe this exact checklist.
3. Create one usable record per system or meaningful use
Use a stable name and record enough context for someone outside the immediate team to identify the system and follow its evidence. NIST’s artifact examples include system documentation, incident response plans, data dictionaries, links to implementation software or source code, and names and contact information for relevant AI actors. Adapt the fields to your organization’s purpose; NIST does not mandate a universal template.
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| Record field | What to capture |
|---|---|
| Identity and purpose | Stable name, short description, intended purpose, and business process or use. |
| Status and scope | Proposed, pilot, production, or retired status, plus the deployment or organizational context that explains why it is in scope. |
| People and accountability | Internal owner and relevant AI actors, with contact details where appropriate. |
| Provider or implementation | Provider information or an implementation reference, as applicable. |
| Supporting artifacts | Links to system documentation, data dictionaries, software or source code, and incident response plans where available. |
| Review or refresh information | Last-reviewed date and last-refreshed date where known; do not imply a date is current if it has not been confirmed. |
4. Name a maintainer and define updates
Assign a specific individual or team to maintain the inventory. State how changes—such as a new deployment, changed purpose, retirement, or updated implementation—are reported and reviewed. Without a named maintainer and an update route, the inventory can quickly stop reflecting what the company actually uses.
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5. Triage without losing portfolio visibility
Keep the broad inventory as the company’s map, then use risk priorities and high-stakes context to identify systems for deeper review. NIST allows risk-prioritized minimum coverage when full coverage is not immediately feasible; that does not make undocumented scope disappear. Record what has not yet been inventoried so leaders can see the gap.
6. Check whether a reviewer can follow the record
Choose representative entries and ask their owners to locate the linked artifacts and explain the system’s status and use. Check whether the inventory can answer both specific questions—such as when a model was last refreshed—and portfolio questions, such as how many systems are deployed or how many users may be affected. Track missing owners, unclear scope, absent links, and stale review dates as gaps rather than silently treating them as complete. These are practical preparation checks, not a NIST-prescribed audit test.
Choose a format that can be maintained
A spreadsheet, an existing asset register, or a dedicated workflow platform can each hold an inventory; the right format depends on how well it supports the work. NIST does not rank or endorse these approaches. Compare them against the needs implied by its guidance:
- Coverage: Can teams capture all in-scope systems, including pilots and third-party tools?
- Ownership: Can each record name a responsible maintainer and relevant contacts?
- Evidence links: Can a reviewer follow documentation, data dictionaries, implementation references, and incident records?
- Maintenance: Can status and review information be updated as systems change?
- Portfolio answers: Can the organization answer questions across the inventory, rather than opening records one at a time?
Use the inventory appropriately in audit preparation
Use the inventory to establish what the organization knows, who is responsible, where supporting artifacts are held, and which gaps remain. Then conduct the risk and control reviews required by the organization’s audit scope. Applicable legal requirements vary by jurisdiction, sector, and system use; NIST AI RMF is voluntary guidance, not a determination of those obligations. NIST released AI RMF 1.0 on January 26, 2023, and says the framework is being revised; the Playbook is to be updated after that revision. Current framework information is available from the NIST AI Resource Center.
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