How to Build an AI Risk Register
AI shows up across an organization faster than anyone tracks it — in copilots, in product features, and hidden inside vendor tools — and each use carries its own risks of bias, error, leakage, and attack. An AI risk register turns that sprawl into something you can score, assign, and manage. This guide shows how to build one.
In plain words
Building an AI risk register means listing every AI use case, naming the risks each one carries, scoring them by likelihood and impact, and assigning an owner and a control to each — and PlayCISO has a free browser-based AI risk register tool to structure it.
Cover the AI-specific categories — bias, hallucination, data, security, and compliance — score everything on one consistent scale, give every risk a named owner, and review on a fixed cadence. A register no one revisits is paperwork, not risk management.
Step by step
- 1
Inventory your AI use cases
List every place AI is used or planned — customer-facing chatbots, internal copilots, document processing, decision support, embedded model features, and third-party AI inside SaaS tools. For each, note what it does, who owns it, what data it touches, and whether its output influences a decision about a person. You cannot assess risk you have not catalogued, and shadow AI inside vendor products is the entry most teams miss.
- 2
Identify the risks for each use case
For every use case, work through the AI-specific risk categories: bias and unfair outcomes, hallucination and factual error, data risks (privacy, leakage, training-data misuse), security risks (prompt injection, model theft, adversarial inputs), and compliance and legal exposure. A chatbot that answers HR questions carries different risks than a model that scores loan applications, so tie each risk to the concrete harm it could cause in that context.
- 3
Score likelihood and impact
Rate each risk on how likely it is to occur and how severe the consequence would be, using a consistent scale (for example 1–5 on each axis) so risks are comparable across use cases. Likelihood × impact gives a single priority score that lets you sort the register and focus effort where it matters. The exact scale matters less than applying the same one everywhere.
- 4
Assign an owner to every risk
Each risk needs one named owner accountable for managing it — not a team, a person. The owner decides on controls, tracks status, and is answerable if the risk materializes. Risks without a clear owner are the ones that sit unaddressed, so an entry is not complete until someone's name is against it.
- 5
Define controls and the treatment decision
For each risk, record how you will treat it: mitigate (add a control such as human review, output filtering, or access limits), transfer (contract or insure), accept (document that leadership accepts it), or avoid (do not build the use case). Capture the specific control and its status, and note the residual risk that remains after the control is in place so no one mistakes "mitigated" for "gone."
- 6
Set a review cadence and keep it live
An AI risk register goes stale fast because models, data, regulations, and usage all change. Set a fixed review cadence, and additionally review whenever a use case is added, a model is upgraded, or an incident occurs. Track each risk's status over time so you can show how the risk posture is trending — a register that is never revisited is documentation, not risk management.
Capture your AI use cases, their risks, likelihood and impact scores, owners, and controls in a structured register — free in your browser, so you start from a working template instead of a blank sheet.
Related
The broader program a risk register slots into.
AI security risks explainedA deeper look at the risk categories you will enumerate in the register.
LLM Threat Model toolDrill into the technical risks of a specific LLM use case the register flags.
All PlayCISO toolsScanners and builders for AI governance and security.
Frequently asked questions
What is an AI risk register?
An AI risk register is a living record of the risks posed by an organization's AI use cases, each scored for likelihood and impact and assigned an owner and a treatment. It is the AI-specific version of the risk register used in enterprise risk management, extended to cover risks like bias, hallucination, and model security. Its purpose is to make AI risk visible, comparable, and accountable rather than vague.
What risks should an AI risk register cover?
At minimum: bias and unfair outcomes, hallucination and factual error, data risks such as privacy and leakage, security risks like prompt injection and adversarial manipulation, and compliance and legal exposure. The right set depends on the use case — a model that affects decisions about people carries heavier fairness and compliance risk than an internal drafting aid. Tie each risk to the specific harm it could cause in context.
How do I score AI risks?
Rate each risk on likelihood (how probable it is) and impact (how severe the consequence), using one consistent scale across the whole register — a 1-to-5 scale on each axis is common. Multiplying the two gives a priority score you can sort on. Consistency is what makes the scores useful; the precise scale you pick is secondary.
Who should own an AI risk?
A single named individual, not a team or a department. The owner is accountable for choosing controls, tracking the risk's status, and answering for it if it materializes. Risks with no clear owner are the ones that go unmanaged, so every entry should have a name against it before you consider it complete.
How often should I review the register?
Set a fixed cadence and also review on events: a new AI use case, a model upgrade, a regulatory change, or an incident. AI risk shifts quickly as models, data, and rules change, so a register reviewed once a year is effectively out of date. Tracking status over time also lets you show whether your risk posture is improving or worsening.
Is an AI risk register the same as a threat model?
No, they complement each other. A risk register is a governance-level view across all your AI use cases, scoring and assigning ownership for risks including non-technical ones like bias and compliance. A threat model is a deep technical analysis of how one specific system could be attacked. The register tells you which use cases deserve a threat model; the threat model tells you how a given one actually fails.