Lightweight AI Risk Register: Practical Template and Guide

Create a compact AI risk register that clarifies risks, owners, and controls so teams act faster and stay compliant — download the simple template and start today.

Small teams and product groups need a focused, actionable way to track AI risks without heavyweight governance. A lightweight AI risk register captures essential details, prioritizes high-impact items, and assigns clear ownership so mitigation happens.

• Quick, usable template to record AI risks and controls.
• How to scope, score, and prioritize risks for rapid action.
• Common AI-specific categories and pitfalls to avoid.
• Compact implementation checklist to deploy within days.

Quick answer

Use a lightweight AI risk register when you need a minimum-viable tool to log model, data, and deployment risks, assign owners, prioritize by impact/likelihood, and track mitigation tasks without heavy process overhead.

When to use a lightweight AI risk register

Opt for a lightweight register when speed and clarity matter: early product development, pilot deployments, small teams lacking formal governance, or when an organization wants rapid risk visibility before scaling controls.

It’s ideal where risks exist but full enterprise GRC systems would slow delivery. The register is a living artifact: start small, iterate, and feed summary items to broader governance later.

Define scope, roles, and stakeholders

Before logging risks, clarify what falls inside your register and who participates.

• Scope: systems, models, datasets, deployment stages (dev/staging/prod), APIs, and user touchpoints.
• Roles: risk owner (single accountable person), model owner (technical lead), data steward, compliance owner, product manager, and reviewer(s).
• Stakeholders: legal, security, UX, customer support, and affected business units.

Example: Scope = customer-facing recommendation models in production; Roles = product manager (owner), ML engineer (implementer), security lead (advisor).

Essential template fields (minimum viable register)

Keep fields minimal but sufficient to act. Use a spreadsheet, lightweight issue tracker, or simple wiki table.

Identify AI-specific risk categories

AI brings predictable risk themes. Categorize risks to make controls repeatable and discover gaps.

• Data quality & lineage: incomplete, biased, or stale training data; unknown provenance.
• Model performance: accuracy drift, overfitting, or distribution shift.
• Fairness & bias: disparate impact across protected groups.
• Privacy: personally identifiable information leakage, model inversion risks.
• Security: adversarial inputs, model extraction, supply chain vulnerabilities.
• Explainability & transparency: inability to explain decisions to regulators/customers.
• Operational: reliability, latency, capacity, monitoring gaps.
• Regulatory & legal: non-compliance with sector rules or contracts.

Tag each register entry with one or more of these categories for reporting and metric tracking.

Assess, score, and prioritize risks

Use a simple scoring model: Impact x Likelihood = Priority. Keep scales small (e.g., 1–5) to reduce ambiguity.

• Impact: 1 (negligible) to 5 (severe legal/safety outcomes).
• Likelihood: 1 (rare) to 5 (very likely given current controls).
• Priority score = Impact × Likelihood; categorize as Low (1–6), Medium (7–12), High (13–25).

Example: Impact 4 (regulatory), Likelihood 3 (possible) → Score 12 → Medium priority. Use priority to schedule mitigation sprints and resource allocation.

Assign controls, ownership, and timelines

Controls should be specific, measurable, and timebound. Each control must have an owner and a due date.

• Control types: technical (retraining, input validation), process (review gates, approval steps), and organizational (policy updates, training).
• Ensure one accountable owner per risk and at least one implementer for each control.
• Use short timelines for high-priority items (weeks to a quarter) and longer cycles for systemic changes (quarters to year).

Example control entry: “Implement input-sanitization middleware (technical) — ML engineer — due in 3 weeks — status: in progress.”

Common pitfalls and how to avoid them

• Too many fields causing abandoned register — keep to the minimum necessary; prune quarterly.
• Unclear ownership — assign a single accountable owner and list implementers.
• Vague controls — define measurable acceptance criteria and evidence (tests, dashboards).
• Ignoring drift — add scheduled model performance checks and data sampling as controls.
• One-off fixes only — prefer systemic mitigations where feasible and log recurring issues as separate risks.
• No stakeholder engagement — review register items in short cross-functional meetings (15–30 minutes).

Implementation checklist

• Create the register template (spreadsheet or tracker) with the essential fields.
• Define scope and list initial models/datasets to include.
• Assign roles and owners for register maintenance.
• Log the top 10 immediate risks using the scoring method.
• Assign controls, owners, and realistic timelines for high-priority items.
• Schedule recurring reviews (biweekly or monthly) and a quarterly cleanup.
• Feed high-severity items into enterprise GRC if required.

FAQ