GOVERN-1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities.

To meet GOVERN-1.6: mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. requirement, you must maintain a complete, continuously updated inventory of AI systems across the enterprise and fund/staff the inventory process based on risk tier. Operationalize it by defining what counts as “AI,” setting mandatory intake triggers, assigning owners, and proving ongoing operation with audit-ready evidence. ¹

Key takeaways:

• You need an AI system inventory mechanism, not a one-time spreadsheet, with defined triggers, owners, and a repeatable workflow. ¹
• Inventory work must be resourced by risk, so high-impact systems get deeper coverage and faster updates than low-risk use cases. ¹
• Auditors and customers will ask for evidence of operation: completeness checks, change logs, exceptions, and remediation tracking. ²

GOVERN-1.6 is a governance requirement that becomes painful only when you treat it as documentation. A real AI inventory is an operating control: it tells you what AI exists, where it runs, who owns it, how it changes, and which obligations attach to it. Without that, you cannot consistently apply model risk management, privacy review, security testing, third-party diligence, or incident response to AI-enabled products and internal automations.

For a CCO, GRC lead, or compliance officer, the fastest path is to convert the requirement into a control you can run: define scope (“what is an AI system here?”), require registration at specific lifecycle events (build, buy, deploy, materially change), and connect the inventory to risk tiers that drive resourcing. “Resourced” needs to be explicit: ownership, staffing capacity, defined SLAs for intake and updates, and time reserved for periodic completeness checks.

This page gives a requirement-level implementation blueprint: who must participate, the minimum data fields to capture, the workflow that keeps the inventory current, and the evidence bundle you should retain for audit and customer due diligence. ²

Regulatory text

Requirement (verbatim): “Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities.” ¹

Operator interpretation: You must (1) implement a mechanism that reliably identifies and records AI systems across the organization, and (2) allocate people/time/tools so the mechanism runs more rigorously for higher-risk AI than for lower-risk AI. A static list is not a mechanism. A mechanism has defined intake triggers, accountable owners, required fields, exception handling, and periodic checks that prove the inventory stays current. ¹

Plain-English interpretation (what this means in practice)

• You need a single source of truth for AI systems that compliance, security, legal, and product teams can query.
• “AI systems” should include built, bought, and embedded AI: internal models, vendor models accessed via API, features using ML scoring, and AI used in business operations (HR, finance, customer support).
• “Resourced according to risk” means you must prioritize effort. A customer-facing AI that makes eligibility decisions should have tighter inventory controls and faster update requirements than a low-risk internal summarization tool.

Who it applies to

Entity types and contexts

• Organizations developing AI systems (product teams, data science teams, engineering) that train, fine-tune, or deploy models. ¹
• Organizations deploying AI systems (business units adopting third-party AI, internal automation teams) even when the model is external. ¹
• Service organizations that provide AI-enabled services to customers or operate AI on behalf of customers, where inventory is foundational for contractual and assurance obligations. ¹

Operational touchpoints

• Product intake and SDLC
• Procurement and third-party onboarding
• Security architecture review
• Privacy/data protection impact assessments
• Change management and release management
• Incident response and customer notifications

What you actually need to do (step-by-step)

1) Define “AI system” for inventory scope

Write a short, testable definition that covers:

• Models you train or fine-tune
• Models you call via API (third-party hosted)
• AI embedded in software you purchase (including “AI-assisted” modules)
• Decisioning, ranking, prediction, generation, or detection functions that affect customers, employees, or regulated operations

Practical tip: Include a “borderline examples” section (e.g., rules engines, statistics-only features) and decide whether you inventory them or document exclusions. This reduces debates during audits and prevents teams from rebranding AI to avoid governance.

2) Choose your inventory mechanism (tooling + workflow)

Pick one primary system of record:

• GRC platform object/table
• CMDB extension
• Dedicated AI governance register
• Lightweight option: controlled spreadsheet plus ticketing workflow (acceptable early, risky at scale)

The mechanism must include:

• Intake form (mandatory fields)
• Approval/verification step (risk owner or governance reviewer)
• Change log (what changed, when, who approved)
• Search and reporting (by risk tier, business unit, third party, data sensitivity)

3) Establish mandatory trigger events (make registration unavoidable)

Define triggers and route them through existing gates:

• Build: model training start, model registered in model registry, or first internal deployment
• Buy: procurement intake for third-party AI tools or AI features in software
• Deploy: production release, feature flag enablement, new region rollout
• Change: model replacement, major prompt/policy change, dataset change, new use case, drift mitigation changes
• Retire: decommission, vendor offboarding

Make it enforceable: Require an inventory ID before production launch (or before purchase approval). Put that requirement into SDLC checklists and procurement playbooks.

4) Define the minimum inventory fields (the “AI system record”)

Keep this tight but sufficient for downstream governance:

Core identification

• System name, description, business owner, technical owner
• Status (planned/dev/prod/retired)
• Environment(s) and endpoints

Sourcing and supply chain

• Built in-house vs third-party vs hybrid
• Third party name(s) and model/service identifiers
• Hosting location (internal, cloud account, vendor-hosted)

Use and impact

• Intended purpose and user population
• Customer-facing vs internal
• Whether outputs materially influence decisions (eligibility, pricing, fraud, HR actions)

Data and security

• Data categories used (including personal data flag)
• Access controls owner and logging location

Risk tier

• Your tiering label (e.g., High/Medium/Low) and why
• Links to required assessments by tier (privacy, security, model risk review)

5) Tie resourcing to risk priorities (what “resourced” looks like)

Document a simple resourcing model aligned to your risk tiers:

• High-risk tier: governance review required before launch; periodic re-attestation; completeness validation by control owner; escalation path for missed updates.
• Lower-risk tier: self-attestation with spot checks; lighter evidence bundle; longer re-attestation cycle if your program allows it.

Resourcing must be visible in:

• Named control owner (RACI)
• Defined operational capacity (who processes intakes, who runs completeness checks)
• Queue management (ticketing, SLAs, escalation)
• Budget/tooling decisions when volume grows

6) Run control health checks (prove ongoing operation)

At a set cadence, run checks to find AI that escaped registration:

• Compare procurement records for AI-related purchases to inventory entries
• Compare cloud resources/model registries to inventory
• Sample production apps for AI endpoints or model artifacts
• Query code repositories for known AI libraries or API calls (as a detective control)

Track findings to closure with due dates and verified remediation. This is where many programs fail: they build the register but never prove completeness over time. ²

Required evidence and artifacts to retain

Maintain an evidence bundle that shows both design and operating effectiveness:

Control design

• Control card/runbook: objective, scope, owner, triggers, steps, exceptions
• Definition of “AI system” and inclusion/exclusion criteria
• Risk tiering rubric and required reviews by tier ²

Control operation

• Export of current AI inventory with timestamps
• Intake tickets/forms for new systems and material changes
• Approval records (who approved risk tier and registration)
• Change log for updates and retirements
• Results of completeness checks (reconciliations, sampling, gap list)
• Remediation tracker with closure evidence ²

Retention note: Keep evidence in a location your audit team can access without heroic effort. A shared drive with no versioning becomes a credibility issue fast.

Common exam/audit questions and hangups

Auditors, customers, and internal risk committees tend to focus on the same pressure points:

1. Completeness: “How do you know this inventory includes all AI in production?”
2. Definition: “What do you classify as an AI system, and what is excluded?”
3. Ownership: “Who is accountable for inventory accuracy per business unit?”
4. Change control: “What triggers an update, and how do you detect unreported changes?”
5. Resourcing: “Show staffing, workflow metrics, or governance cadence aligned to risk tiers.” ¹

Frequent implementation mistakes (and how to avoid them)

1. Mistake: Treating the inventory as a one-time discovery project.
   Fix: Build intake triggers into procurement and SDLC so new AI cannot launch unregistered.

2. Mistake: Inventorying only “models,” not AI-enabled systems.
   Fix: Inventory at the system/use-case level and link to underlying models and vendors.

3. Mistake: Risk tiering exists on paper but doesn’t change workload.
   Fix: Define tier-based requirements (who reviews, what evidence is required, how often re-attested) and show a queue/workflow that enforces it. ¹

4. Mistake: No detective controls to find shadow AI.
   Fix: Run periodic reconciliations against procurement, cloud, and code signals; track gaps to closure.

5. Mistake: Exceptions handled informally.
   Fix: Add an exception path with approver, expiration date, and compensating controls.

Enforcement context and risk implications

No public enforcement cases were provided for this specific NIST AI RMF requirement in the supplied sources. Practically, the risk shows up in three places:

• Regulatory and contractual credibility: You cannot demonstrate governance over AI you cannot enumerate.
• Incident response and notifications: Unknown AI systems delay containment, impact analysis, and stakeholder communications.
• Third-party risk management: If business units adopt third-party AI without registration, you lose visibility into data sharing, model updates, and subprocessor chains. ²

A practical 30/60/90-day execution plan

First 30 days (stand up the minimum viable mechanism)

• Name the control owner and publish the AI system definition.
• Create the AI inventory register with required fields and an intake form.
• Add two hard gates: procurement intake and production release require an inventory ID.
• Pilot with one product team and one business ops function; capture feedback and tighten fields. ¹

By 60 days (make it real across the enterprise)

• Roll out the workflow to all business units; train procurement, security review, and product ops on triggers.
• Implement risk tiering and map tier to required reviews/evidence.
• Start a remediation backlog for already-deployed AI found during discovery and register them with owners. ²

By 90 days (prove operating effectiveness)

• Run your first completeness health check (procurement vs inventory, plus a technical sampling).
• Publish metrics internally: new registrations, updates, exceptions, overdue items by tier.
• Close the loop with governance: recurring forum to approve exceptions and review gaps.
• If scale is stressing manual workflows, evaluate tooling. Daydream can help by centralizing AI inventory, connecting third-party records, and generating an audit-ready evidence bundle from the workflow history. ²

Frequently Asked Questions

Do we have to inventory internal “AI-assisted” tools used by employees (like summarization or meeting notes)?

If the tool processes organizational data or affects regulated operations, include it in scope and tier it by impact. If you exclude certain classes, document the exclusion criteria and apply detective checks for drift.

What counts as “mechanisms” versus a spreadsheet?

A spreadsheet can be part of a mechanism if it is controlled, has defined triggers, an owner, approvals, change history, and periodic completeness checks. The mechanism fails when updates depend on goodwill and memory. ¹

How do we inventory third-party AI embedded in software we buy?

Add AI identification questions to procurement intake and require the third party to disclose AI features, model update practices, and data flows. Create an inventory entry even if you never see the underlying model.

Who should own the AI inventory: Compliance, Security, or Engineering?

Compliance/GRC typically owns the control and evidence, while business and technical owners are accountable for accurate system records. Security and privacy should be required reviewers for higher-risk tiers.

How do we handle fast-changing prompt-based systems where prompts change weekly?

Define “material change” thresholds for when a prompt or policy update requires inventory update and re-review. Track prompt changes in change management and link the change record to the inventory entry.

We have multiple model registries and CMDBs. Do we need one tool?

You need one system of record for governance decisions, with links to technical sources. If you cannot consolidate tools yet, standardize identifiers and build reconciliations so completeness checks are defensible. ²

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0