AI use-case inventory and classification

The ai use-case inventory and classification requirement under ISO/IEC 42001 means you must keep a current, auditable list of every AI use case you develop or operate, and assign each one a documented risk level that drives governance actions (review depth, approvals, monitoring, and change control). Build the inventory first, then standardize the classification rules, and tie both to your AI lifecycle gates.

Key takeaways:

• You need a single source of truth for all AI use cases, including third-party and “shadow AI,” with clear ownership.
• Risk classification must be consistent, explainable, and connected to required controls (approvals, testing, monitoring, and exceptions).
• Audits tend to fail on coverage (missing use cases) and traceability (risk tier not linked to evidence and decision records).

For most organizations, “inventory” sounds simple until you try to answer basic questions quickly: Which teams have AI in production? Which models touch personal data? Which use cases can materially affect customers, employees, or regulated decisions? ISO/IEC 42001 expects you to have those answers in a controlled way, supported by evidence, not tribal knowledge ¹.

This page translates the ai use-case inventory and classification requirement into an operator-ready approach. The goal is not a spreadsheet that looks good once, but an inventory that stays accurate as teams deploy new features, swap model providers, retrain models, or expand to new geographies. The classification portion matters because it determines governance intensity: a low-risk internal productivity chatbot should not run the same process as an AI system that impacts eligibility, pricing, fraud outcomes, hiring, or safety-related decisions.

If you already run security and privacy programs, treat this as the AI equivalent of an application/service inventory plus a risk tiering model. If you do not, start small: define what counts as an AI use case in your organization, stand up intake, and make the inventory the “front door” for approvals, testing, and ongoing monitoring.

Requirement: AI use-case inventory and classification (ISO/IEC 42001)

ISO/IEC 42001’s governance intent includes maintaining visibility into AI you build or run, and applying risk-based controls ¹. Practically, that becomes two linked mechanisms:

1. An AI use-case inventory (complete coverage, kept current)
2. A risk classification scheme (repeatable, documented criteria with outcomes)

Your program is “operational” when the inventory is the system of record and the risk tier triggers defined actions.

Regulatory text

Provided excerpt (summary-level): “Baseline implementation-intent summary derived from publicly available framework overviews; licensed standard text is not reproduced in this record.” ¹
Implementation intent summary: Maintain an inventory of AI use cases and classify associated risk levels. ¹

What the operator must do:

• Keep a controlled register of AI use cases across the organization (including AI developed in-house, configured from third parties, or embedded in SaaS).
• Assign a risk level for each use case using defined criteria.
• Maintain evidence that classification drives governance actions (approval, testing, monitoring, and changes).

Plain-English interpretation

You need to know “what AI exists,” “where it runs,” “what it does,” “what data it touches,” and “what harm it could cause,” and you need to show that higher-risk AI gets tighter controls. Examiners and auditors typically care less about your exact tier labels and more about whether your criteria are consistent and your coverage is credible.

Who it applies to

Entity scope

• AI developers: teams building or fine-tuning models, creating AI features, training pipelines, prompt workflows, or decisioning logic. ¹
• AI system operators: teams deploying, integrating, configuring, or using AI systems in operations, including third-party AI tools and APIs. ¹

Operational context where this shows up

• Product teams shipping AI-enabled functionality.
• Internal operations using AI for HR, legal, finance, customer support, security, or engineering productivity.
• Procurement and IT onboarding AI-capable third-party tools.
• Data platforms enabling model training, evaluation, and telemetry.

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

Step 1: Define “AI use case” for inventory purposes

Write a short definition your teams can apply consistently. Include:

• Model-based systems (ML, deep learning, generative AI).
• Rule+model hybrids where model output affects decisions.
• Third-party AI where you configure prompts, policies, or decision thresholds.

Set a policy expectation: “No AI in production without an inventory record and risk tier.”

Step 2: Decide inventory ownership and intake “front door”

Assign:

• Business owner (accountable for purpose and outcomes)
• Technical owner (accountable for implementation and monitoring)
• Risk owner (GRC/privacy/security sign-off as required)

Establish intake triggers:

• New AI feature or internal deployment
• Material change (new data source, new model/provider, retraining approach, new user population)
• Expansion to new jurisdiction or regulated use

Step 3: Build the minimum viable inventory schema (fields that auditors expect)

Start with fields you can populate now, then expand. Minimum set:

• Use case name and description (plain language)
• Business purpose and user group
• AI type (e.g., predictive model, LLM workflow)
• Deployment status (idea, pilot, production, retired)
• System boundaries and integrations (upstream data, downstream decisions)
• Data categories processed (include personal/sensitive where relevant)
• Third parties involved (model provider, platform, annotators)
• Applicable policies/standards mapping (privacy, security, records, model risk)
• Risk tier and rationale
• Required approvals and dates
• Links to key evidence (testing, monitoring, incident history)

Practical tip: keep “links to evidence” as URLs/attachments in the record so the inventory becomes a navigation layer for audits.

Step 4: Define a risk classification model you can defend

Use a small set of tiers (example: low/medium/high) and document criteria. Your criteria should consider:

• Impact severity: Could outputs affect legal status, access, safety, employment, financial outcomes, or vulnerable groups?
• Data sensitivity: Does it process personal, sensitive, or confidential data?
• Autonomy: Is the AI advisory, or does it make/execute decisions without meaningful human review?
• Scale and exposure: Number of impacted users, customer-facing vs internal, criticality of the process.
• Model uncertainty: Known limitations, lack of explainability requirements, drift likelihood.
• Third-party dependency risk: external model changes, lack of transparency, contractual limits.

Make the classification outcome actionable. Example mapping:

• Low: documented use case, basic testing, lightweight monitoring, standard change control.
• Medium: added approval gate, pre-release evaluation results attached, defined monitoring KPIs, periodic review.
• High: formal risk assessment, senior approval, enhanced testing (bias/robustness where relevant), incident playbooks, tighter access controls, stricter change management, and ongoing review cadence.

Avoid “checkbox tiers.” If a tier doesn’t change anything, auditors will treat classification as cosmetic.

Step 5: Run a discovery sweep to populate the initial inventory

You will miss things if you rely on self-report alone. Combine:

• Procurement/AP: contracts and expense data for AI tools
• SSO/IdP logs: AI SaaS usage
• Cloud spend tags: GPU, model endpoints, ML platforms
• Code scanning: model artifacts, LLM SDKs, API keys
• Business unit attestations: quarterly or release-based

Record gaps as exceptions with owners and closure dates.

Step 6: Tie the inventory to lifecycle gates

Make the inventory record required for:

• Architecture review / security review
• Privacy review / DPIA-style analysis (if applicable internally)
• Model validation / evaluation sign-off
• Production change approval
• Incident management and postmortems

This is where tools like Daydream fit naturally: capture intake, standardize risk tiers, and keep an audit-ready system of record that links approvals, evidence, and change history without hunting through email threads.

Step 7: Maintain it (change control + periodic review)

Define what updates the record:

• Model/provider change
• Training data change
• Prompt/policy change that affects behavior
• New downstream use
• Monitoring alerts and incidents
• Retirement/decommission

Make inventory updates part of the same ticket/SDLC workflow that shipped the change.

Required evidence and artifacts to retain

Auditors will ask for “show me” items. Keep these attached or linked per use case:

Inventory artifacts

• AI use-case register export (current snapshot)
• Data dictionary for inventory fields and definitions
• RACI/ownership list

Classification artifacts

• Risk tiering procedure and decision criteria
• Completed classification rationale per use case
• Exception process and approved exceptions (with expiry)

Operational linkage

• Approval records (who approved, when, scope)
• Testing/evaluation summaries appropriate to tier
• Monitoring plan and sample monitoring outputs
• Change log demonstrating updates after material changes
• Third-party due diligence records for AI-related providers (where applicable)

Common exam/audit questions and hangups

Expect questions like:

• “How do you know this inventory is complete?” (coverage and discovery evidence)
• “Show AI systems in production and their risk tiers.” (production list must reconcile to reality)
• “Pick one high-risk use case. Show the full chain: intake → classification → approvals → testing → monitoring.” (traceability)
• “How do you handle third-party AI embedded in tools?” (scope definition and intake triggers)
• “What constitutes a ‘material change’?” (change control clarity)

Hangups that stall audits:

• No consistent definition of “AI use case.”
• Multiple inventories (security has one, product has another) with conflicts.
• Risk tier assigned but no evidence of tier-driven controls.

Frequent implementation mistakes and how to avoid them

1. Inventory only covers “models,” not “use cases.”
   Fix: inventory the business use case plus the technical system. One model can support several use cases with different risks.

2. Shadow AI excluded (“it’s just productivity”).
   Fix: include internal tools and LLM assistants, then tier them low/medium with clear guardrails.

3. Classification criteria are subjective.
   Fix: convert judgment into prompts/checklists with required fields (impact type, data type, autonomy, user population). Require rationale text.

4. No linkage to SDLC or procurement.
   Fix: make inventory ID a required field in release tickets and third-party onboarding workflows.

5. Stale inventory.
   Fix: define material change triggers and implement periodic attestations by owners; treat non-response as a risk issue.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Even without case citations, the risk pattern is consistent: incomplete inventories cause control failures downstream. If you cannot prove what AI exists and what it does, you cannot credibly manage privacy, security, bias, incident response, or third-party obligations across AI deployments ¹.

Practical 30/60/90-day execution plan

Days 0–30: Stand up the foundation

• Name an inventory owner in GRC and a technical counterpart in engineering.
• Publish the “what counts as an AI use case” definition and the intake requirement.
• Create the inventory template (minimum viable fields) in your GRC system or Daydream.
• Draft the risk tier criteria and required actions per tier.
• Pilot intake with one business unit and one internal AI tool category.

Days 31–60: Populate and validate coverage

• Run discovery across procurement, SSO, and engineering repositories.
• Normalize use-case records; assign owners for each.
• Classify initial risk tiers with documented rationales.
• Implement an exceptions workflow for missing info, with time-bound remediation tasks.
• Pick two high-risk candidates and run a deep traceability drill (end-to-end evidence).

Days 61–90: Operationalize and harden

• Integrate inventory ID into SDLC release gates and third-party onboarding.
• Add change triggers and a standard “material change” checklist.
• Implement monitoring-plan fields and require them for medium/high tiers.
• Train product, engineering, and procurement on intake and classification.
• Run an internal audit-style review: sample records, test evidence links, verify production reality against inventory.

Frequently Asked Questions

What counts as an “AI use case” versus a model or tool?

Treat the use case as the business purpose plus the system behavior and deployment context. A single model can power multiple use cases, and each use case may warrant a different risk tier.

Do we need to inventory third-party AI tools like SaaS copilots and embedded AI features?

Yes, if they perform AI functions in your environment or affect your data, users, or decisions. Track them as AI use cases with a third-party dependency noted and classify risk based on impact and data exposure ¹.

How detailed does the risk classification need to be?

Keep tiers simple, but make criteria specific enough that two reviewers reach the same result most of the time. Auditors look for consistency, rationale, and evidence that the tier changes the required controls.

Who should approve the risk tier?

The business owner should propose it, and an independent risk function (GRC/privacy/security) should confirm it for medium/high tiers. For high-risk uses, require senior sign-off aligned to your governance model.

How do we handle “shadow AI” adoption that teams don’t report?

Combine self-attestation with discovery signals (procurement, SSO logs, cloud usage, code scanning). Treat missing use cases as a control gap with tracked remediation, not as an informal request.

We already have an application inventory. Can we extend it instead of creating a new one?

Yes, if you can add AI-specific fields (purpose, data categories, autonomy, model/provider, evaluation, monitoring, risk tier) and enforce intake/change triggers. The key is audit-ready traceability from the inventory record to approvals and evidence.

Related compliance topics

• 2025 SEC Marketing Rule Examination Focus Areas
• Access and identity controls
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Footnotes

1. ISO/IEC 42001 overview