An AI risk register is the operational foundation of any AI governance program. It documents every AI system your organization uses, the risks those systems pose, and the controls in place to manage those risks. This guide explains what an AI risk register must contain, how to structure risk classifications, and how to keep the register current.
What an AI risk register is — and is not
An AI risk register is a structured, living document that records: every AI system the organization uses in consequential operations; the risk assessment for each system; the current risk classification; the controls implemented; and the review schedule. It is an operational record, not a one-time deliverable.
It is not the same as a general IT asset inventory. An IT asset inventory tracks software licenses, hardware, and infrastructure. An AI risk register goes deeper — capturing how each system makes decisions, what data it uses, what harms it could cause, and what governance mechanisms are in place to prevent or mitigate those harms.
Required fields under TRAIGA
TRAIGA and comparable AI governance frameworks require the following information for each registered AI system:
| Field | Description | Required for compliance |
|---|---|---|
| System name | Unique identifier for the system | Yes |
| Purpose / use case | What the system does and what decisions it influences | Yes |
| Vendor / developer | Who built the system (internal, third-party, or hybrid) | Yes |
| Data types processed | Categories of data the system uses (biometric, health, financial, etc.) | Yes |
| Affected populations | Who is subject to AI-influenced decisions from this system | Yes |
| Risk classification | LOW / MODERATE / HIGH / CRITICAL with documented rationale | Yes |
| Governance controls | Controls implemented for this system, with status and due dates | Yes |
| Human oversight | Whether human review is available before consequential decisions are finalized | Yes |
| Last review date | When the most recent risk assessment was completed | Yes |
| Next review due | Scheduled date for the next assessment | Recommended |
| Deployment type | Cloud, on-prem, hybrid, or embedded | Recommended |
Structuring risk classifications
Risk classifications must be deterministic and auditable. A regulator reviewing your register must be able to trace each classification to specific, documented criteria — not subjective judgment or committee consensus.
A compliant classification framework evaluates the following factors:
- Consequential decision scope: Does the system make or assist in decisions that materially affect individuals?
- Sensitive data processing: Does the system process biometric data, health information, financial data, or other sensitive categories?
- Vulnerable populations: Does the system interact with or make decisions about minors, elderly individuals, disabled individuals, or low-income populations?
- Reversibility of adverse outcomes: If the system makes an error, how difficult is it to detect and correct?
- Scale of impact: How many individuals are subject to the system's decisions?
- Human oversight availability: Is there a human review mechanism before final decisions are made?
- System maturity: Has the system been tested for bias, accuracy, and drift? How well-characterized are its failure modes?
Keeping the register current
A register that was accurate at the time of your last compliance project but has not been updated since provides no compliance protection — and may make things worse by creating evidence of governance neglect.
The register must be updated when any of the following occur:
- A new AI system is deployed or evaluated for deployment
- An existing system is significantly updated or retrained
- The use case of an existing system changes
- A new vendor is engaged for AI capabilities
- An AI incident is logged and investigated
- A scheduled review cycle comes due
In practice, this means the register is never “done.” Treating the initial registration as a one-time project and failing to maintain the register is one of the most common — and most penalized — AI governance failures.
Spreadsheet vs. purpose-built platform
Many organizations start with a spreadsheet. This is a reasonable first step for organizations with very few AI systems, but it does not scale and creates specific compliance gaps:
No immutable audit trail
Spreadsheets can be edited without a record of who changed what, when, and why. Regulators require an auditable history.
Cannot auto-generate disclosures
TRAIGA requires disclosure statements that accurately reflect current system documentation. Generating these manually from a spreadsheet is slow and error-prone.
No automated review scheduling
Spreadsheets cannot proactively alert teams when reviews are due or overdue.
Cannot enforce control completion
Tracking control implementation status across dozens of systems in a spreadsheet results in controls going unmonitored and uncompleted.
No audit-ready export
When a regulator requests your governance documentation, a collection of spreadsheets is not an audit-ready package.
Building your register
Start by identifying every AI system your organization uses in consequential decisions. Cast wide — it is better to over-include and narrow down than to miss a covered system. Register each system with the required fields, conduct a structured risk assessment, and implement the required controls.
The TRAIGA platform automates every step of this process — from initial system registration through risk assessment, control tracking, disclosure generation, and executive certification. Organizations using the platform typically complete their initial register in days, not months.