AI Risk Management Policy

Field	Detail
Organisation	[Organisation Name]
Document type	Policy — approved, mandatory
Framework basis	EU AI Act Art. 9 · ISO/IEC 23894 · ISO 31000 · NIST AI RMF (MAP/MEASURE/MANAGE)
Owner	[Policy owner — e.g. AI Governance Lead]
Approved by	[Accountable executive / Board]
Version / status	v1.0 — DRAFT for adoption
Effective date	[Date]
Next review	[Date — at least annually]
Classification	Internal
Prepared with	autogovern.io — AI governance & risk templates

1. Purpose

This policy defines how [Organisation Name] identifies, analyses, evaluates, treats, monitors and reports the risks arising from its use of artificial intelligence (AI), across the whole lifecycle and as a continuous process — not a one-off exercise. It operationalises the risk commitments in the AI Governance Policy.

2. Objectives

• Prevent AI from causing harm to people, groups, the organisation or the public.
• Keep AI risk within the organisation’s stated risk appetite.
• Provide a consistent, repeatable method to assess and treat AI risk.
• Meet legal risk-management obligations (e.g. EU AI Act Art. 9) and align to ISO/IEC 23894 and NIST AI RMF.
• Give leadership clear, current visibility of the AI risk profile.

3. Scope

This policy applies to all AI systems in scope of the AI Governance Policy — in-house, procured, embedded, generative and agentic — and to everyone involved in their lifecycle. It covers risks to health, safety and fundamental rights, and risks to security, privacy, fairness, performance, operations, reputation and compliance.

4. Definitions

Term	Meaning
Risk	The effect of uncertainty on objectives — here, the likelihood and severity of AI-related harm.
Inherent risk	Risk before controls are applied.
Residual risk	Risk remaining after controls are applied.
Likelihood	How probable the risk is to occur (scored 1–5).
Impact / severity	How serious the harm would be if it occurred (scored 1–5).
Risk appetite	The amount and type of risk the organisation is willing to accept.
Risk tolerance	The acceptable variation around the appetite for a specific risk.
KRI	Key Risk Indicator — a metric that signals rising risk.
Circuit-breaker	A control that pauses or falls back a system when a threshold is breached.

5. Risk governance — three lines of defence

Line	Owner	Role in AI risk
1st line	System / Model owners, Product, ML/Eng	Own and manage risk in their systems day-to-day; maintain the risk register entries; apply controls.
2nd line	Risk, Compliance, DPO, Security	Set the framework, challenge risk ratings, monitor appetite, and provide oversight.
3rd line	Internal Audit / independent review	Provide independent assurance that AI risk is managed effectively.

6. Risk appetite & tolerance

[Organisation Name] will not deploy AI with unacceptable residual risk to health, safety or fundamental rights. Residual high risks require documented risk acceptance signed off by the accountable executive before go-live. The organisation states its appetite per risk category (see below) and reviews it at least annually.

Risk category	Appetite (example — set your own)
Harm to people / fundamental rights	Very low — high/unacceptable residual risk blocks deployment
Unfair bias / discrimination	Low — must pass a fairness gate before release
Security & data breach	Low
Performance / accuracy degradation	Moderate, within monitored thresholds
Reputational	Low

7. AI risk taxonomy

[Organisation Name] assesses AI risk across these categories:

Category	Examples
Safety	Physical or financial harm from wrong or unsafe outputs/actions
Bias & fairness	Discriminatory outcomes across protected or vulnerable groups
Privacy & data protection	Unlawful use, leakage or inference of personal data
Security	Prompt injection, model/data poisoning, model theft, insecure output handling
Transparency & explainability	Opaque decisions, undisclosed AI use, unexplainable outcomes
Robustness & performance	Errors, hallucinations, drift, out-of-distribution failure
Autonomy / agentic	Unintended or irreversible actions by AI agents; scope creep
Societal & ethical	Manipulation, misinformation, environmental impact
Operational & third-party	Vendor dependency, availability, supply-chain and integration risk
Legal & compliance	Breach of AI, privacy, sector or consumer law

8. Risk-management process (continuous)

Aligned to ISO 31000 / ISO/IEC 23894, NIST AI RMF (MAP → MEASURE → MANAGE) and EU AI Act Art. 9:

1. Establish context — intended purpose, users, deployment environment, affected people, and reasonably foreseeable misuse.
2. Identify — enumerate risks across the taxonomy above, including to fundamental rights.
3. Analyse — score each risk by likelihood × impact (1–5 each) to get an inherent score.
4. Evaluate — compare against appetite; decide treat / tolerate / transfer / terminate.
5. Treat — assign a mitigation/control and an owner; record residual risk.
6. Monitor & review — track KRIs; re-assess at every material model/data change and at least quarterly.
7. Report — surface the risk profile to the AI Governance Committee and leadership.

9. Risk assessment methodology (5×5)

Score likelihood and impact 1–5; the risk score is their product (1–25).

Score	Likelihood	Impact / severity
1	Rare	Negligible
2	Unlikely	Minor
3	Possible	Moderate
4	Likely	Major
5	Almost certain	Severe / critical (harm to people, major legal breach)

Rating bands: 1–4 Low · 5–9 Medium · 10–14 High · 15–25 Critical.

Band	Response
Low (1–4)	Accept and monitor
Medium (5–9)	Treat where cost-effective; owner monitors
High (10–14)	Treat before/at deployment; owner + 2nd-line review
Critical (15–25)	Do not deploy until reduced; executive risk acceptance required if residual remains high

10. Risk treatment — the four T’s

Option	When to use	Example
Treat	Reduce likelihood or impact with controls	Add human review, guardrails, testing, monitoring
Tolerate	Accept within appetite with monitoring	Low residual risk, documented acceptance
Transfer	Share the risk	Insurance, contractual allocation to a vendor
Terminate	Avoid the risk	Do not build/deploy, or withdraw the system

11. Key Risk Indicators (KRIs) & monitoring

Monitor leading indicators in production and act on thresholds:

KRI	Example threshold	Response
Data drift (PSI per key feature)	> 0.2	Investigate; assess retraining
Performance drop vs. baseline	> 5%	Trigger review / circuit-breaker
Fairness — disparate impact ratio	< 0.80 (four-fifths rule)	Investigate and remediate before continued use
Guardrail / policy breach rate	[set threshold]	Tighten controls; root-cause
Prompt-injection / jailbreak rate	[set threshold]	Harden inputs & output handling
Human-override rate spike	[set threshold]	Investigate root cause

12. Agentic & autonomous AI

For AI systems that take actions (not just produce outputs), apply additional controls proportionate to the potential impact of those actions:

• Least-privilege scopes — grant agents the minimum tools, data and permissions needed.
• Approval gates — require human approval for irreversible, high-value or sensitive actions.
• Action logging — record every action, tool call and decision for audit.
• Kill switch — a tested mechanism to halt the agent immediately.
• Bounded autonomy — define what the agent may and may not do without escalation.

13. Incident & escalation management

Any risk scoring ≥ 15 inherent, or any newly discovered harm to people, is escalated to the AI Governance Committee within 5 working days. Serious incidents are handled under the AI Incident Response Plan, including regulatory reporting where required:

Situation (EU AI Act Art. 73)	Report to authority within
Default serious incident	15 days of awareness
Death of a person	10 days
Widespread infringement or serious disruption of critical infrastructure	2 days

14. Third-party & supply-chain risk

Assess the AI risk introduced by vendors and embedded models: security posture, data handling, model provenance, availability, and their own compliance. Record third-party systems in the risk register with a named internal owner and contractual risk allocation.

15. Reporting & assurance

The risk register is the single source of truth for AI risk. The AI Governance Lead reports the risk profile — open risks by severity, trend, and KRI breaches — to the AI Governance Committee at each meeting and, at summary level, to the Board. Independent review (3rd line) assures the process at least annually.

Regulatory & framework mapping

This policy is designed to help the organisation align with the following instruments. Confirm which apply to your systems and jurisdictions.

Framework / law	Jurisdiction	What this policy supports
EU AI Act (Reg. 2024/1689)	EU / EEA	Risk-management (Art. 9), data governance (Art. 10), documentation (Art. 11), logging (Art. 12), transparency (Art. 13), human oversight (Art. 14), accuracy/robustness (Art. 15), prohibited practices (Art. 5), transparency to users (Art. 50)
NIST AI RMF 1.0	US / global	GOVERN, MAP, MEASURE and MANAGE functions
ISO/IEC 42001	Global	AI management system (AIMS) — leadership, planning, operation, evaluation, improvement
ISO/IEC 23894 / ISO 31000	Global	AI risk management guidance and principles
GDPR (incl. Art. 22)	EU / UK	Lawful basis, DPIAs, safeguards for solely-automated decisions
PIPEDA + OPC gen-AI principles	Canada (federal)	Valid consent for training data, transparency, accountability
Quebec Law 25 (s. 12.1)	Canada — Quebec	Notice, disclosure of factors, and human review of exclusively-automated decisions
OSFI Guideline E-23 (eff. 2027)	Canada (financial)	Model risk management across the lifecycle, incl. AI/ML
TBS Directive on ADM + AIA	Canada (federal gov)	Algorithmic Impact Assessment, notice, explanation, human intervention

17. Review

This policy and the risk methodology are reviewed at least annually and after any serious incident or material regulatory change.

Appendix A — AI risk register (maintain this table)

ID	Risk / harm	Category	Cause	Likelihood (1-5)	Impact (1-5)	Inherent	Control / mitigation	Owner	Residual	Status
R-01	[e.g. biased outcomes for a protected group]	Bias	[cause]	3	4	12	[control]	[owner]	6	Open
R-02

Appendix B — Risk treatment plan (per material risk)

Field	Detail
Risk ID	[R-xx]
Chosen option (4 T’s)	[treat/tolerate/transfer/terminate]
Actions & controls	[list]
Owner	[name]
Due date	[date]
Residual score after treatment	[score]
Accepted by (if residual high)	[executive]

Appendix C — Linked documents

This policy is supported by: the AI Governance Policy, Bias & Fairness Audit Procedure, Post-Market Monitoring & Drift Runbook, Accuracy/Robustness/Security Test Plan, Human Oversight Procedure, and AI Incident Response Plan.

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Document control & approval

Version	Date	Author	Approved by	Summary of change
1.0	[Date]	[Author]	[Approver]	Initial adoption

Sign-off

• Policy owner: __________________________ Signature: ______________ Date: __________
• Accountable executive: __________________________ Signature: ______________ Date: __________
• Next review due: __________

This template was generated by autogovern.io as a professional starting point. Review and adapt it to your organisation, systems, sector and legal advice before adoption. It is an educational aid, not legal advice.