How to build an AI use register (and why you need one)

Why you need an AI use register

Your teams are already using AI — in SaaS tools, embedded features, and copilots — often faster than anyone can track. An AI use register is the single source of truth that answers the questions leadership, auditors, and regulators will ask:

• Which AI tools and use cases are in use, and by whom?
• What data and decisions does each one touch?
• Who approved it, on what basis, and what safeguards are in place?

Without a register, "AI governance" is a slide deck. With one, it's a system of record you can act on and defend.

What to capture for each use case

A useful register goes beyond a tool name. For every AI use case, record:

• Purpose & owner — what it does, the business reason, and a named accountable owner.
• Tool & vendor — the underlying product, and its data-use, training, and retention terms.
• Data — the categories involved, and whether any is personal or regulated.
• Decision impact & automation level — does it advise, or decide? Does it affect individuals?
• Human oversight — who reviews outputs, and how.
• Risk tier — a consistent, explainable rating (see below).
• Status & review date — proposed, in review, approved, or retired — and when it's next reviewed.

How to start in a week

1. Seed it. List the obvious tools first (chat assistants, coding copilots, analytics AI).
2. Open a submission path. Let anyone register a use case in minutes, so shadow AI surfaces instead of hiding.
3. Score risk consistently. Use simple, versioned rules (personal data? affects individuals? level of automation?) to assign a tier — deterministic and explainable beats gut feel.
4. Route the risky ones. Send higher-risk use cases for human review and safeguards before they ship.
5. Set review dates. Governance is not one-and-done; schedule periodic re-review.

Common mistakes to avoid

• A spreadsheet. It goes stale, has no workflow, no audit trail, and no access control.
• Inventorying tools but not use cases. The same tool can be low-risk for one task and high-risk for another.
• No owner. Accountability can't sit with "the AI" or the vendor.
• No evidence. If you can't show the decision and its rationale later, it didn't happen.

From register to audit-ready

The register is the foundation. Once it's in place, you can layer on risk assessment, an approval workflow, policies, incident tracking, and audit-ready reporting mapped to frameworks like NIST AI RMF and ISO/IEC 42001.

That's exactly what AI Assurance Hub is built to do — inventory, assess, approve, and evidence every AI use case in one place. Explore a live demo or start free.