An effective AI governance policy turns your organisation’s values and risk tolerance into operating rules. It should say which AI systems and uses are covered, what is allowed or banned, who approves each use, how risks are assessed, what data and security safeguards apply, when people must review or be told about an AI decision, and how systems are monitored, investigated and retired.
The policy is useful only when employees can act on it. Pair the principles with an AI inventory, named owners, a proportionate approval workflow, an escalation route, training and scheduled reviews. Apply legal requirements to the jurisdictions, system categories and organisational roles that actually apply; a single generic rule cannot replace that analysis.
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Start with purpose, scope and definitions
Open with the policy’s purpose: responsible use of AI that supports organisational goals while protecting people, information, rights and the organisation’s ability to operate. State the policy owner, approval authority and effective date, then define how it relates to information-security, privacy, procurement, records-management, employment and product-safety policies.
Who and what is covered
Specify whether the policy applies to employees, contractors, temporary staff, board members, subsidiaries and third parties acting for the organisation. Include every business unit and geography in scope, not only the team that built a model.
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Define an AI system broadly enough to capture machine-learning models, generative systems, recommendation engines, scoring tools, computer-vision systems, automated decision services and software that embeds a model. Include systems the organisation develops, buys, configures, fine-tunes, hosts or merely connects to. NIST’s AI Risk Management Framework (AI RMF) is designed for organisations that design, develop, deploy, evaluate or acquire AI, including third-party software, hardware and data.
Define the lifecycle
Describe the stages covered by the policy: idea and procurement, data preparation, development or configuration, testing, deployment, operation, change, suspension and retirement. This prevents a control that exists at launch from disappearing when a vendor changes a model or a team repurposes an approved tool.
Translate values into risk tiers
A list of principles such as fairness, privacy and safety is not an approval system. Convert those values into risk criteria tied to your organisation’s risk tolerance and priorities. NIST says governance should align risk-management activity with organisational risk tolerance; your policy should therefore define what evidence and authority each level requires.
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|---|---|---|---|
| Low | Internal productivity use with no sensitive data and no effect on an individual’s rights, access or eligibility | Approved tool list, basic data checks, user training and record in the inventory | Designated business owner |
| Moderate | External content, personal data, customer-facing recommendations or operational decisions that receive human review | Documented purpose and limitations, privacy and security review, testing, monitoring plan and named reviewer | Business owner plus risk, privacy or security reviewer as applicable |
| High | Use that can materially affect employment, access to services, safety, legal interests or vulnerable people; or a system with significant uncertainty or autonomy | Multidisciplinary impact assessment, documented data and model evidence, independent testing where feasible, strong human oversight, incident plan, supplier due diligence and periodic reassessment | Executive or committee approval before deployment |
| Prohibited | Use barred by law, organisational policy or an unacceptable level of harm that cannot be mitigated | Do not deploy; block access and report attempted use where appropriate | Policy owner or compliance function handles exceptions and enforcement |
Treat these tiers as a starting structure, not a legal classification. Document the factors that move a use upward: sensitivity of the data, number and vulnerability of affected people, reversibility of the outcome, degree of automation, scale, model uncertainty, potential discrimination, safety impact and dependence on a supplier.
List approved, restricted and prohibited uses
Employees need concrete examples and a route to ask questions. Maintain three lists, reviewed when laws, tools or business processes change.
Approved uses
- Drafting or summarising non-confidential internal material when a person checks the output.
- Brainstorming, translation or coding assistance with information classified for that use.
- Low-risk analytics where the result is advisory and a trained employee validates it before action.
Restricted uses
- Processing personal, confidential, regulated or commercially sensitive data.
- Generating content sent to customers, regulators, applicants or the public.
- Recommendations or rankings that influence hiring, performance, credit, insurance, education, healthcare, housing, benefits, access or safety.
- Autonomous actions in production systems, financial processes, identity systems or other environments where an error is difficult to reverse.
Require prior approval, documented safeguards and meaningful human review for restricted uses. Explain what the reviewer must check, what evidence they receive and how they can reject or override an output.
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Prohibited uses
Ban uses that violate applicable law, contractual commitments, privacy obligations, intellectual-property rights, security rules or the organisation’s ethical limits. Examples can include entering protected information into an unapproved public service, using AI to make a high-impact decision without required oversight, evading access controls, fabricating records or deploying a system after a material risk has been identified and not mitigated. State that an employee must stop and escalate when the classification is unclear; silence is not approval.
Assign accountability and escalation
Name people, not just departments. At minimum, identify an executive sponsor, policy owner, business owner for each use, technical owner, risk or compliance contact, privacy and security reviewers, procurement lead and incident manager. For high-impact uses, identify the human overseer who has authority to pause the system and the trained staff who review individual outputs.
Define the approval record: requester, intended purpose, affected groups, tier, evidence reviewed, conditions, approvers, launch date, review date and expiry if approval is temporary. Set communication lines for material risk, exceptions, incidents and changes. Include an escalation path that works outside normal office hours when a system can affect safety, security or essential services.
Separate oversight roles
- Operator: uses the system and follows task-level instructions.
- Reviewer: checks outputs, investigates uncertainty and can override a result.
- Owner: is accountable for purpose, resources, monitoring and withdrawal.
- Risk approver: confirms that evidence and mitigations match the tier.
- Incident lead: coordinates containment, notification, remediation and lessons learned.
Require a proportionate impact assessment
Before use, record the intended purpose, expected benefits, affected people, foreseeable harms, limitations, uncertainty, alternatives and mitigations. Reassess after a significant model, data, supplier, interface, user group or context change. A multidisciplinary review is appropriate when impacts extend beyond one specialist area.
The assessment should answer:
- What decision or task does the system support, and what is outside its purpose?
- Who may be affected directly or indirectly, including vulnerable groups?
- What could go wrong, how severe would it be, and how reversible is the outcome?
- What assumptions, data gaps, error rates or uncertainty could change the result?
- What human intervention, appeal, fallback and compensation mechanisms exist?
- Which legal, contractual, sectoral or geographic requirements apply?
Keep the assessment with the approval record and link it to test results, model or vendor documentation, operating procedures and review dates.
Set rules for data, privacy, security and intellectual property
Map each approved use to your data-classification scheme. State which data may be entered, transformed, retained or exported, and which systems are prohibited. Require least-privilege access, approved accounts, encryption and retention limits appropriate to the classification. A privacy review should address purpose limitation, lawful basis where relevant, notices, data-subject rights, cross-border transfers, logging and deletion.
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Security review should cover authentication, access control, secrets, prompt or input manipulation, model and data integrity, supply-chain dependencies, output handling, abuse monitoring and recovery. Require testing for foreseeable misuse and a process to patch or replace a vulnerable component.
Intellectual-property controls must cover training and input data, generated output, licenses, confidentiality, attribution and contractual restrictions. NIST specifically identifies third-party risks, including infringement of another party’s intellectual-property or other rights. Do not assume that a supplier’s terms transfer every risk to the supplier; record responsibilities and remedies in the contract.
Define human oversight and transparency
“Human in the loop” is not enough. Specify the reviewer’s competence, time, information, authority and independence. A reviewer must be able to understand the system’s purpose and known limitations, identify an unreliable output, seek additional evidence, reject or override a recommendation and stop use when controls fail.
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State when users, customers, applicants or other affected people are told that AI is involved, what the system does, the role of human review and how to challenge an outcome. The European Commission’s AI Act guidance describes specific transparency and oversight duties in defined situations, including obligations for certain high-risk systems. Map those duties to your actual role—provider, deployer or another actor—and to the system’s legal category rather than applying a blanket statement to every AI use.
Maintain an AI inventory and documentation set
Create a single register, or connected registers, for all AI systems and uses. NIST’s GOVERN 1.6 calls for inventory mechanisms resourced according to risk priorities. At minimum, record:
- System name, version, supplier, hosting location and dependencies.
- Business owner, technical owner, reviewer and escalation contacts.
- Purpose, users, affected groups, data categories and geographic scope.
- Risk tier, legal classification, approval decision and conditions.
- Model, dataset, configuration, interface and supplier documentation.
- Test evidence, known limitations, monitoring metrics and incident history.
- Next review date, change triggers, retention period and retirement plan.
Documentation supports transparency, human review and accountability. Keep records understandable to the people who must operate and challenge the system, not only to developers.
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Control testing, monitoring and change
Set pre-deployment evaluation expectations that match the tier. Test accuracy or task performance, robustness, security, privacy, bias or disparate impact where relevant, and the system’s behavior on edge cases and out-of-scope inputs. Record the test population, conditions, thresholds, unresolved limitations and the decision to accept or mitigate residual risk.
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After deployment, monitor performance, drift, misuse, complaints, override rates, unusual inputs, security events and changes in the surrounding process. Define thresholds that trigger investigation, notification, temporary suspension or withdrawal. Material changes should trigger reassessment; examples include a new model version, training data, provider, data source, user group, decision context, autonomy level or jurisdiction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build incident response and remediation into the policy
Give every employee a simple reporting route for incidents and near misses. The report should capture the system, time, affected people, input and output, action taken, evidence preserved and whether harm is continuing. The triage process should classify severity, assign an owner and decide whether to restrict or suspend use.
For a material event, preserve logs and relevant data, contain the system, assess affected decisions, correct or reverse outcomes where possible, communicate with affected people and notify authorities or partners when required. High-risk systems may carry specific duties to act on risks and serious incidents. After containment, document root causes, corrective actions, owner and deadline, then feed the findings into future assessments, training and supplier reviews.
Manage suppliers and the AI supply chain
Procurement must review vendors, models, datasets, subcontractors, hosting, update practices, service levels, audit rights, data use, confidentiality, security, incident notification, portability and termination. Identify dependencies whose failure could disrupt a high-risk process and maintain a fallback, manual procedure or alternative provider.
Require suppliers to notify you about material model, data, policy and security changes. Keep a record of the evidence you received and the checks your organisation performed; a vendor brochure is not a substitute for your own risk decision.
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Train staff, govern exceptions and enforce the rules
Provide role-specific training. General users need data-handling, verification, disclosure and reporting instructions. Reviewers need domain knowledge, bias and uncertainty awareness, override authority and escalation practice. Owners and approvers need risk assessment, documentation, monitoring and incident duties. Train relevant partners as well as employees; NIST includes training for personnel and partners who fulfill assigned AI risk-management responsibilities.
Exceptions should be rare, time-limited and documented with the requester, rationale, risk, safeguards, approver, owner, expiry date and review conditions. State consequences for bypassing approval, misusing data, concealing an incident or ignoring a stop instruction. Apply enforcement consistently and provide a safe channel for good-faith concerns.
Review, update and retire the policy and systems
Assign a policy owner and a fixed review cadence, supplemented by triggers for legal change, incidents, new capabilities, significant model updates, supplier changes or a new business context. Record decisions and communicate revisions to affected staff.
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How to align the policy with recognised frameworks and law
NIST AI RMF 1.0 was released on 26 January 2023 and is voluntary. NIST says the framework is being revised and has noted a 2026 concept note for a critical-infrastructure profile. Use it as a structure for governance and risk-management outcomes, not as a universal law or proof of compliance.
The NIST-hosted crosswalk maps AI RMF governance outcomes to ISO/IEC FDIS 42001 clauses, including risk assessment and treatment, review, inventory, roles and responsibilities. The crosswalk can help integrate AI controls with an existing management system, but it does not establish certification or legal compliance. Compare the frameworks by legal status, intended scope, fit with current management processes, implementation effort and evidence or assurance needs.
The European Commission’s AI Act FAQ describes requirements for providers and deployers of high-risk AI, including risk management, data quality, documentation and traceability, transparency, human oversight, accuracy, cybersecurity, robustness and monitoring. The FAQ accessed on 13 October 2026 states a general application date of 2 August 2026, with some provisions applying earlier and high-risk provisions subject to later dates; it also describes a 2026 measure that can extend certain deadlines. Dates and duties are system- and role-specific, so verify the current consolidated text and implementation guidance before making a time-sensitive compliance claim.
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A practical approval workflow
- Register the idea: record the purpose, users, data, supplier, geography and intended decision or task.
- Classify the risk: assess affected people, sensitivity, autonomy, reversibility, uncertainty and legal category.
- Review impacts: document harms, limitations, alternatives, mitigations and required specialist input.
- Check controls: complete privacy, security, intellectual-property, procurement and human-oversight reviews.
- Test and approve: retain evaluation evidence, conditions, named owners, approval authority and review date.
- Deploy with safeguards: train users, publish operating instructions, enable logging and provide an escalation route.
- Monitor and reassess: review metrics, complaints, drift, incidents and material changes against defined thresholds.
- Pause or retire: suspend use when controls fail, remediate, record the decision and decommission safely when the use ends.
NIST’s AI RMF Core describes governance as “a continual and intrinsic requirement for effective AI risk management over an AI system’s lifespan and the organization’s hierarchy.” A policy built around accountable people, a maintained inventory, proportionate review and lifecycle controls turns that principle into daily practice.
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