How to Build an AI Risk Register for Your Organisation

What this means

Most organisations hold a diffuse sense that AI carries risk, but that sense rarely translates into managed action because it is never written down in a form that can be assigned and tracked. A risk register fixes this. It lists each AI risk as a discrete entry, scores it, names who owns it, and records what is being done about it.

The act of building the register is itself valuable. It surfaces risks people had only half-articulated, exposes which ones currently have no owner or control, and creates a shared, concrete picture of the organisation's actual AI risk position.

Why it matters for business

A risk register is the bridge between governance intention and governance practice. IBM's research found mature governance strongly associated with higher AI returns, and a register is a core artefact of that maturity. It is also what allows leadership and boards to exercise real oversight: instead of a general unease, they see a ranked list of specific risks and the state of their controls.

For Australian organisations, the register also supports compliance and due diligence. When a regulator, auditor, insurer or major client asks how AI risk is managed, a maintained register with owners and controls is direct, credible evidence — far stronger than assurances that risk is "considered".

How it works technically

A practical AI risk register captures, for each risk:

Common AI-specific entries include data leakage, hallucination in high-stakes outputs, prompt injection, biased or discriminatory outputs, privacy breaches, over-reliance by staff, vendor lock-in and model or provider failure. The register should connect to the governance workflow, so that new use cases feed risks into it, and to the responsible AI infrastructure, which provides the monitoring evidence that controls are working.

Practical implementation considerations

The register should be a living document, reviewed on a regular cadence and updated as use cases and the AI landscape change. A register created once and shelved provides documentation but not management.

Edison AI's AI readiness audit produces an initial AI risk register as a deliverable — a populated, scored and owned list specific to the organisation — which gives leadership an immediate, concrete view of where the real exposures are and which controls are missing.

Ownership is the feature that makes a register work. A risk with no named owner is not managed; it is merely noted. Every entry should map to a person accountable for its controls and residual level.

Common mistakes

• No register at all. AI risk stays diffuse and unmanaged, surfacing only when something goes wrong.
• Risks without owners. Unowned risks are documented but not actually managed.
• A static document. AI risk evolves quickly; an unreviewed register becomes inaccurate.
• Vague entries. "AI might make mistakes" is not actionable; "AI may state incorrect figures in client-facing reports without review" is.
• Disconnected from controls. A register that lists risks but not the controls and their effectiveness cannot guide action.

What leaders should do next

Build an initial AI risk register now, even a simple one, listing your most significant AI risks with a likelihood, impact, owner and current controls for each. Assign every risk to an accountable individual. Review and update the register on a set cadence and whenever new use cases are approved. Connect it to your governance workflow so risks are captured as they arise, and use it as the standing artefact through which leadership and the board oversee AI risk. The goal is risk that is named, owned and managed rather than felt and feared.

Start with an AI readiness audit to map your data, access and governance gaps before you scale.