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.
Why a register is the foundation of AI governance
Most governance controls — impact assessments, oversight processes, access reviews — require you to know what you have before they can work. Without an inventory, AI systems accumulate invisibly: a department adopts a chatbot, a developer integrates an API, a vendor embeds a recommendation model, and none of it is coordinated. An accountability register makes that visible and creates a single source of truth that auditors, regulators, and internal governance bodies can reference.
Frameworks that specifically call for or imply such an inventory include the EU AI Act (which requires documentation and registration for high-risk systems), ISO 42001 (the AI management system standard), the NIST AI Risk Management Framework, and the UK ICO’s guidance on explaining AI decisions. A register built now is proportionate groundwork for whichever framework becomes mandatory in your jurisdiction.
What each register row should contain
A useful register row captures at minimum:
- System name and purpose — what it does and where it is used
- Risk classification — minimal, limited, high, or unacceptable risk under a framework like the EU AI Act; or internal tiers such as low / medium / high
- Data processed — categories (personal data, special category data, financial data, health data)
- Responsible owner — a named individual, not a team
- Review date — the next scheduled review, with a frequency tied to risk level
- Compliance status — whether required assessments have been completed
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 for keeping the register useful
- 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.
- Set high-risk systems to quarterly reviews. Lower-risk systems can be annual, but high-risk deployments should be reviewed more frequently as the technology and regulation both move fast.