AI Risk Register Template for Small and Mid-Sized Teams

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Most smaller firms do not have an AI problem first. They have a visibility problem. Staff are already using AI for drafts, summaries, search and workflow automation, yet many teams still cannot say where the main risks sit.

A practical AI risk register template fixes that gap. It gives you a working record of AI use, the harm that could follow, and the person responsible for reducing it. The first step is to decide what belongs in the register.

Why smaller teams need an AI risk register now

Small and mid-sized organisations rarely need an enterprise governance programme on day one. They need a short, current record of where AI is in use, what could go wrong, and who owns the response.

That matters because the common problems in 2026 are not exotic. They are privacy leaks, made-up answers, security gaps, vendor dependency, weak access control and compliance mistakes. Those issues appear in ordinary work, such as drafting a proposal, searching internal files, or connecting an AI agent to email and CRM.

A register brings those risks into the open. It also stops one tool being treated as one risk. The same assistant might draft customer replies, summarise contracts and search HR documents. Each use case has a different risk profile, so each deserves its own row.

Broader frameworks can help with the shape of the work. The NIST AI Risk Management Framework is a good reference if you want a recognised model behind a lighter process. Still, most smaller teams do not need a long policy before they act. A shared spreadsheet can be enough if people use it.

If nobody owns a risk, nobody reduces it.

That is why ownership matters more than perfect scoring. A useful register creates line of sight, not paperwork.

What a practical register should include

Start with the use case, not the tool name. “Chatbot” is too vague. “Drafting support replies with customer order data” tells you far more about risk, impact and control.

Next, write the risk as a full sentence. Good entries use plain language, such as “Customer data could be pasted into prompts and retained by the provider” or “The model could invent figures in a client proposal”. That format makes the row easier to review with legal, IT, operations and leadership.

Keep the scoring simple. Most smaller teams do well with a 1 to 5 score for likelihood and a 1 to 5 score for impact. Multiply them for a total risk score. In practice, 1 to 5 is low, 6 to 12 is medium, and 15 to 25 is high. Do not waste time arguing over whether a risk is a 3 or a 4. Consistency matters more than precision.

Add columns for current controls, owner, next action, review date and status. Those fields turn a static note into a working risk log. Without a review date, the register goes stale. Without a next action, the row becomes a parking place.

It also helps to flag rows that touch privacy, finance, intellectual property, regulated activity or employment decisions. Rules differ by sector and market, so the register should trigger review by the right people rather than guess at legal answers.

If you want a practical bridge between a formal framework and a small-team setup, this plain-English guide to the NIST framework is useful background.

Copy-and-paste AI risk register template

A simple register works well in Excel, Google Sheets, Notion or your project tracker. Keep one row per risk, not one row per tool.

Risk ID	AI use case	Risk statement	Likelihood (1-5)	Impact (1-5)	Score	Current controls	Owner	Next action	Review date	Status
[AI-01]	[Task or workflow]	[What could go wrong, and what harm follows?]	[1-5]	[1-5]	[L x I]	[Controls already in place]	[Name or role]	[Single next mitigation step]	[dd mmm yyyy]	[Open / Monitoring / Closed]

Keep the first version lean. If a field does not help someone act, cut it. For most teams, the strongest columns are the use case, risk statement, score, owner and next action.

Separate “current controls” from “next action”. Those are often mixed together, and that hides what is already working. Also, if one platform is used across sales, finance and HR, create separate rows. The tool is the same, but the harm is not.

Worked example for a growing team

The example below fits a 75-person services firm using AI for support, proposal writing, internal search and light workflow automation. Your entries will differ, but the pattern will hold.

When AI connects to email, files or CRM, treat it as part of your attack surface. This AI security risk management overview is a helpful reminder that connected tools need the same discipline as any other system.

Risk ID	AI use case	Risk statement	Score	Current controls	Owner	Next action	Status
AI-01	Support drafting	Customer data could enter prompts and be retained outside approved systems	16	Approved tool, staff guidance	Ops manager	Turn off model training, add redaction rule	Open
AI-02	Proposal drafting	The model could invent facts, pricing or case studies in client material	12	Human review before send	Sales lead	Add source check and approval step	Open
AI-03	Lead scoring	Biased outputs could skew outreach or screening decisions	10	Manual review of edge cases	Revenue ops lead	Test sample outputs each month	Monitoring
AI-04	AI agent linked to email and CRM	A stolen token or weak scope could expose customer systems and records	15	SSO, MFA, audit logs	IT lead	Limit scopes, rotate tokens, review logs weekly	Open
AI-05	Internal knowledge assistant	Staff could access contracts, payroll or HR files outside their role	15	Basic folder permissions	IT lead	Tighten role-based access and test monthly	Open
AI-06	Contract summarisation	Output could miss privacy, retention or sector rules	12	Final legal review for regulated documents	Compliance lead	Define banned use cases and review triggers	Monitoring
AI-07	Single vendor dependency	Price rises, outages or term changes could halt a core workflow	9	Export prompts, manual fallback	Operations director	Test a second provider and exit plan	Monitoring

Notice where the highest scores sit. Privacy, security and access control often deserve action before more visible issues, because the damage is immediate and hard to unwind.

Hallucinations and bias still matter, yet those risks often drop quickly when you add a clear human review step. Vendor dependency can look less urgent, but it becomes serious when a renewal, outage or policy change interrupts a revenue process.

Start your register this week

You do not need a steering group to begin. A two-hour session with the people who use AI most often is enough for a first draft.

1. List every AI tool, assistant, plug-in and automated workflow used in the last 30 days. Include unofficial use if you know it exists.
2. Group that list by business task. “Proposal drafting” and “customer support replies” are better labels than vendor names.
3. Write one plain-English risk sentence for each task. Use “could” and name the likely harm.
4. Score likelihood and impact on a 1 to 5 scale. Mark anything at 15 or above for action this month.
5. Assign one owner to each row. Then record current controls, one next action and a review date.
6. Review the sheet weekly for the first month, then monthly. Add new rows when staff adopt a new tool, connect a new data source or expand a use case.

Keep the first version short. Ten honest rows beat fifty vague ones. Once the register settles, you can add policy links, incident notes and supplier checks where they help.

Conclusion

AI risk does not become manageable because a model looks impressive. It becomes manageable when your team can see where AI is used, what harm could follow and who is accountable for action.

The strongest move for most smaller firms is visibility. A modest register, kept current, will do more than a polished policy that nobody opens.