What is an AI accountability register?

It is a maintained inventory of every AI system an organization uses or builds, recording purpose, risk, data, owner, and review status. It supports governance frameworks like the EU AI Act and ISO 42001 by making AI use auditable.

Why classify each system by risk?

Risk-based frameworks apply heavier controls to higher-risk systems. Classifying upfront tells you which systems need documentation, human oversight, and impact assessments versus which are low-risk and lightly governed.

Is the register stored anywhere?

No. The register is generated in your browser from what you type and is never uploaded. Copy the output into your own systems to retain it.

How often should a register be reviewed?

Set a review date per system based on risk — high-risk systems quarterly, lower-risk annually. The register includes a review-schedule column so each entry has an owner and a next-review date.

Does this make us compliant?

A register is a foundational artifact for AI governance but not the whole picture. You still need impact assessments, oversight processes, and policies. Treat this as the inventory layer that other controls build on.

AI Accountability Register Template

AI accountability register template

Good AI governance starts with knowing what you actually run. An accountability register is the canonical inventory of every AI system in your organization — each row capturing what the system does, how risky it is, what data it touches, who owns it, and when it was last reviewed. This tool turns a quick list of your systems into a clean, structured register you can drop straight into your governance documentation.

How it works

Enter your organization name, then add each AI system with its purpose, a risk classification, the categories of data it processes, the responsible owner, and a review cadence. The tool assembles each entry into a register and renders it as both a Markdown table and CSV. Compliance status is derived from whether you have recorded an owner and a review date, giving you an at-a-glance view of which entries are still incomplete. Everything runs locally — nothing is uploaded.

Tips and notes

One row per system, not per feature. Group tightly-coupled capabilities; split only when ownership or risk differs.
Be honest about risk. Under-classifying to dodge controls defeats the purpose and creates audit exposure.
Name a human owner. “The data team” is not accountable; a named person is.
Keep it living. A register reviewed once is decoration. Wire reviews into your change process so new AI integrations get added automatically.