AI Use Case Register is an inventory of AI systems and proposed uses, recording what each system does, who owns it, and its risk level. Security and governance teams use it to track approvals, data sources, controls, and monitoring obligations, and to stop shadow AI from bypassing review.
What the register is for
An ai use case Register gives an organisation a single inventory of proposed and live AI uses, so teams can see what each system is intended to do, who owns it, and whether it has been reviewed before use. It turns scattered experimentation into a governed catalogue.
That matters because AI adoption often starts informally, especially when business teams try tools without a formal approval path. A register makes the use case visible early enough for governance, security, legal, and risk teams to decide whether it belongs inside the approved AI portfolio.
What information a use case should capture
A useful register is more than a list of tool names. It should describe the business purpose, the owner, the risk tier, the data sources involved, the control obligations attached to the use case, and any monitoring or review conditions that apply after approval.
Those fields matter because the security posture of an AI system depends on context. The same model can be low risk in one workflow and unacceptable in another if it touches regulated data, makes externally visible decisions, or operates without human oversight.
How the register supports governance
The register is the operating record for AI governance. It helps reviewers confirm that a use case has an accountable owner, that the approval decision is traceable, and that required controls are not left to informal memory or ad hoc email threads.
It also helps organisations connect policy to practice. A register can show whether a use case was assessed against AI Governance Guide expectations, and whether the decision aligned with the organisation’s approved AI policy and risk process.
How it reduces shadow AI and review gaps
One of the main reasons to maintain the register is to stop shadow AI from bypassing review. If an AI use case is not recorded, it is easy for teams to lose track of which tools are in use, what data they receive, and whether monitoring or human review is still required.
The register also creates a control point for reassessment when a use case changes. New data sources, expanded scope, new automation, or a different deployment model can all move a previously approved use case into a higher-risk category that needs fresh review.
Risk and Threat Considerations
An incomplete register creates blind spots, and blind spots are where unmanaged AI use tends to grow. The main risks are unapproved data exposure, missing oversight, inconsistent control enforcement, and stale approvals that no longer match how the system is actually being used.
Failure mechanism: Teams deploy or expand AI use cases outside the register, so governance never sees the real data flows, permissions, or decision impact.
Impact: The organisation can end up with hidden sensitive-data processing, unsupported approvals, weak accountability, and a larger attack surface for misuse or policy bypass.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, while ISO/IEC 42001:2023 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | Govern | AI use case registers support AI governance and risk oversight across the lifecycle. |
| Recommendation — Use the Govern function to maintain an authoritative inventory of AI use cases, owners, and risk decisions. | ||
| ISO/IEC 42001:2023 | 4.1 — Understanding the organization and its context | A use case register records AI context, purpose, and governance scope for the management system. |
| 6.1 — Actions to address risks and opportunities | The register captures AI use-case risks, treatments, and acceptance decisions. | |
| 8.2 — AI risk assessment | Use cases must be assessed before approval, and the register tracks that decision state. | |
| Recommendation — Define and maintain the AI use case register as part of the AI management system context and scope. Tie each registered AI use case to risk treatment, approval, and reassessment decisions. Record the risk assessment outcome for each AI use case before authorising use. | ||
| NIST SP 800-53 Rev 5 | PM-31 — Continuous Monitoring Strategy | The register identifies AI systems that need monitoring, review, and change tracking. |
| Recommendation — Track each AI use case in the monitoring strategy so review obligations stay current. | ||
Practitioner Guidance
Why practitioners should care: The register is only useful if it stays current and is treated as a control record, not a one-time inventory exercise. If ownership, risk tier, or data sources change, the entry should change too.
Governance implication: Treat the register as the source of truth for approval status, monitoring obligations, and retirement decisions, so no AI use case can move from pilot to production without traceable review.
Practitioner takeaway: The best registers are operational, not ceremonial, because they make it harder for AI use to become invisible.
Related resources from NHI Mgmt Group
- How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
- How should organisations centralise AI use case and model inventories?
- What signals show that an AI system is drifting outside its approved use case?
- How should organisations decide whether an AI use case is worth deploying?