You do not need to wait for a new law to govern artificial intelligence responsibly. Start with a voluntary, risk-based policy that assigns decision rights, inventories every AI use, evaluates impact before deployment, requires human oversight, controls suppliers, monitors performance, and retires systems safely. Use the NIST AI Risk Management Framework (AI RMF) 1.0 as a flexible structure, and consider ISO/IEC 42001:2023 if you need a formal management system. Treat legal analysis as a separate workstream: a voluntary framework can improve readiness, but it does not by itself establish compliance with laws that apply to your organization.
What an AI governance policy is—and what it is not
An AI governance policy is the operating system for decisions about AI across its lifecycle. It defines who may propose, buy, build, approve, deploy, monitor, change, investigate, and retire an AI system. It should cover internally developed models, purchased tools, AI features embedded in ordinary software, and services employees use to process organizational or customer information.
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It is not a one-time document filed in a policy library. The NIST AI RMF Core describes governance as “a continual and intrinsic requirement” over an AI system’s lifespan and throughout an organization’s hierarchy. Your policy therefore needs owners, records, review triggers, escalation routes, and funding—not just principles.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteNor is it a substitute for legal advice. Whether a rule applies depends on factors such as country, sector, your role (for example, provider or deployer), the use case, the system’s risk category, effective dates, and evidence duties. The European Commission’s overview of EU AI Act governance and enforcement identifies the AI Office, national market-surveillance authorities, and advisory and national competent bodies; it does not, by itself, determine your organization’s obligations.
Choose a practical voluntary baseline
NIST AI RMF 1.0
NIST released AI RMF 1.0 on January 26, 2023. It is intended to be voluntary, rights-preserving, non-sector-specific, and use-case agnostic. Its four functions give a policy a usable lifecycle:
- Govern: establish accountability, policies, culture, risk tolerance, and oversight.
- Map: understand the purpose, context, affected people, harms, benefits, and dependencies before proceeding.
- Measure: select and perform evaluations that fit the use and its risks.
- Manage: prioritize risks, apply controls, monitor results, respond to incidents, and improve.
The companion NIST AI RMF Playbook contains implementation suggestions, not mandatory steps or a universal checklist. Its page notes that it will be updated as the AI RMF is revised, so check the current version when you adopt or refresh your policy.
ISO/IEC 42001:2023
ISO/IEC 42001:2023 specifies requirements and guidance for establishing, implementing, maintaining, and continually improving an AI management system within an organization. It is a better fit when you want defined management-system controls, documented continual improvement, and potentially an audit-oriented program. Using it does not automatically prove compliance with every law; applicable external requirements still need an independent determination.
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How to decide
| Option | Strength | Best use | Important limitation |
|---|---|---|---|
| NIST AI RMF 1.0 | Flexible, voluntary risk-management structure covering Govern, Map, Measure, and Manage | A first internal process or a program that must adapt across teams and use cases | It does not prescribe one technical test suite, certification path, or legal outcome |
| ISO/IEC 42001:2023 | Formal AI management-system requirements and continual-improvement guidance | Organization-wide governance with mature documentation, assurance, or audit needs | Requires more management-system discipline and does not replace jurisdiction-specific legal analysis |
| Applicable law | Binding duties determined by jurisdiction, role, system category, and use | Legal applicability, mandatory controls, reporting, and evidence decisions | There is no single global rule; a framework or standard cannot decide applicability for you |
Many organizations use NIST as the working structure, ISO/IEC 42001 as a management-system reference, and legal review as a separate control gate.
Build the policy in nine steps
1. Define scope, purpose, and the intake rule
State why the organization uses AI, what outcomes it seeks, and which activities the policy covers. Include employee use of public or third-party services, not only systems that engineering teams operate. Address text, image, audio, video, recommendation, decision-support, predictive, and generative systems where relevant.
Rank #2
Define exclusions narrowly. A low-risk productivity feature may follow a lighter path, but “excluded” should not mean untracked. Create a central inventory and require registration before procurement, development, pilot, or production deployment. Capture the business owner, provider, model or feature name, version, purpose, data categories, users, deployment locations, and current status. An incomplete first inventory is acceptable; an unacknowledged inventory is not.
2. Assign accountable owners and decision rights
Name an executive decision owner who can accept or reject material risk and an operational policy owner who runs the process. Document responsibilities for:
- Business owners, who define the purpose, expected benefit, users, and consequences of failure.
- Technical teams, who document architecture, versions, testing, security, and change management.
- Privacy, security, legal or compliance, and records specialists, who review their respective risks.
- Procurement, which enforces supplier diligence and contract requirements.
- Support and affected-user teams, which receive complaints, correction requests, and escalation signals.
Specify who can approve a pilot, production release, exception, material model change, and shutdown. Publish a communication path for incidents and train each role on the decisions it must make. NIST’s Govern outcomes call for documented responsibilities, workforce and partner training, and executive responsibility for risk decisions.
3. Set principles and risk tolerance
Turn broad values into decisions people can apply. Your policy should state that AI use must have a legitimate purpose, proportionate impact, appropriate data handling, understandable accountability, and a way to challenge or correct harmful outcomes. List prohibited uses or require a heightened approval path for them.
Define risk tiers based on impact rather than model novelty. A system that ranks job candidates, controls access to essential services, or materially affects a person’s finances deserves more scrutiny than an internal drafting assistant. Connect each tier to approval authority, documentation, testing depth, human review, monitoring, and review frequency. Identify who may approve exceptions and what evidence an exception requires.
Rank #3
4. Map context before approval
For every material use case, require a written context and impact assessment. At minimum, record:
- Intended purpose, users, operating environment, and expected benefits.
- People and communities affected, including those who do not directly use the system.
- Data sources, sensitive data, permissions, retention, and geographic movement.
- Your role and the provider’s role, system dependencies, and downstream integrations.
- Foreseeable misuse, failure modes, security threats, and ways a human could be harmed.
- Alternatives considered, including not using AI.
Invite perspectives beyond the development team when the impact warrants it: domain experts, privacy and security staff, accessibility specialists, worker or customer representatives, and affected-user support. Record a proceed, modify, or stop decision with conditions and an owner. NIST’s Map function treats this context and impact work as the basis for an initial go/no-go decision.
5. Choose proportionate measurements and controls
Do not promise one universal test battery. Select controls for the use, the people affected, and the plausible harms. Depending on the system, your control plan may include:
- Accuracy, reliability, robustness, and performance checks against representative data.
- Privacy review, data-minimization checks, access controls, retention limits, and leakage testing.
- Security assessment, abuse testing, prompt-injection or adversarial testing, and dependency review.
- Bias, disparate-impact, accessibility, and human-factors assessment where people may be evaluated or served differently.
- Documentation of limitations, confidence information, known failure cases, and operating boundaries.
- Production monitoring with thresholds that trigger investigation, rollback, additional review, or suspension.
Define what counts as a failed gate, who can waive a gate, and how the waiver is recorded. Keep test data, methods, results, unresolved limitations, and approval conditions with the system record.
6. Specify human oversight and accountability
“Human in the loop” is too vague to govern anything. For each use, state whether a person reviews every output, a sample, or only exceptions; what information the reviewer receives; what expertise and time are required; and what the reviewer is authorized to override.
Identify decisions that must not be finalized automatically, the circumstances that require a pause, and the escalation route for uncertainty or harm. Give users and affected people a way to report an error, request correction where appropriate, and learn how the system influenced an outcome. Log overrides and recurring disagreements so the organization can improve the system or retire it.
7. Control providers and data supply chains
Before buying or integrating an AI service, assess the supplier’s claims, model and data provenance, privacy and security practices, intellectual-property and rights risks, subcontractors, geographic processing, retention, access, change notices, and incident cooperation. Ask what the provider discloses about limitations, evaluation, and material updates.
Put the required controls into contracts where possible: permitted uses, confidentiality, security measures, notification periods, audit or evidence rights, support during incidents, deletion or return of data, and exit assistance. Maintain a contingency plan for a high-risk provider outage, harmful update, withdrawal of service, or refusal to cooperate. NIST’s Govern function expressly includes third-party risks and contingency processes for high-risk third-party data or AI systems.
8. Keep records, monitor, and review
Create a record set that can answer who approved a use, why it was needed, what was tested, which limitations were accepted, what happened in production, and how problems were corrected. At minimum, retain the inventory, context assessment, approval, contracts, data and model documentation, test results, monitoring reports, incidents, corrective actions, exceptions, and retirement decision.
Set a review frequency based on risk and assign an owner; there is no universal cadence established by NIST. Add event-triggered reviews for a material model or data change, a new user group or geography, a new deployment context, an incident, a supplier change, or a change in legal requirements. Monitor both technical behavior and real-world outcomes, including complaints, override rates, error patterns, security events, and whether the system is still delivering its stated benefit.
9. Retire systems safely
Define decommissioning before launch. The retirement procedure should identify who can disable the system, revoke credentials and integrations, handle or delete data, notify users and affected parties, preserve required records, and check for residual risks in downstream processes. Plan for replacement or manual operation when a critical service is withdrawn. NIST includes safe decommissioning and phasing out as part of lifecycle governance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn the policy into an operating workflow
- Register: a requester submits the use, owner, provider, data, users, and intended decision.
- Classify: the policy owner assigns a risk tier and identifies required reviewers.
- Map: the team completes the context, impact, misuse, dependency, and alternative assessment.
- Measure: reviewers approve a proportionate test and monitoring plan; results and limitations are recorded.
- Decide: the authorized body approves, approves with conditions, requests changes, or stops the use.
- Deploy: technical and business owners implement access, human-oversight, logging, training, and user-notice controls.
- Monitor: owners review thresholds, incidents, complaints, overrides, supplier notices, and benefit claims.
- Reassess or retire: trigger review after material change or safely shut down when the purpose, controls, or risk no longer justify operation.
Put the workflow in procurement, engineering, security, privacy, and change-management processes so employees cannot bypass it simply by subscribing to a new tool with a corporate card.
Common failure modes and fixes
| Failure | Why it fails | Policy fix |
|---|---|---|
| “We will wait for the final regulation.” | Untracked uses, weak evidence, and rushed remediation accumulate while rules develop. | Adopt a voluntary baseline now and map legal requirements separately as they become clear. |
| A principles-only statement | Employees cannot tell who approves a use or what to do when risk appears. | Add owners, thresholds, forms, evidence, escalation, and consequences. |
| One approval for every AI system | Low-risk work becomes slow while high-impact uses receive insufficient attention. | Use risk tiers with proportionate review and controls. |
| Ignoring employee experiments | Confidential or personal data may enter services outside procurement and security review. | Include employee tools in scope, provide an approved-use route, and set clear data restrictions. |
| Assuming a supplier manages the risk | Your organization remains accountable for its context, users, and decisions. | Perform diligence, contract controls, evidence collection, monitoring, and contingency planning. |
| “Human oversight” without authority | A reviewer may rubber-stamp outputs or be unable to stop a harmful decision. | Define information, expertise, time, override power, escalation, and stop conditions. |
| No retirement plan | Old models, credentials, copies of data, and downstream automations continue operating. | Require shutdown, access revocation, notification, records, and residual-risk checks. |
How to start in the first 90 days
Days 1–30: establish control
- Name the executive and operational owners.
- Publish an interim rule requiring registration before new AI procurement or deployment.
- Collect a first-pass inventory from procurement, IT, engineering, security, privacy, and business teams.
- Identify prohibited or high-impact uses that require immediate escalation.
- Adopt the four NIST functions as the vocabulary for the program.
Days 31–60: assess the highest risks
- Apply the context and impact assessment to the most consequential systems.
- Set risk tiers, approval authorities, human-oversight requirements, and minimum evidence.
- Review supplier contracts and data flows for high-risk services.
- Define incident intake, user complaints, escalation, and shutdown procedures.
- Train approvers, developers, buyers, managers, and frontline reviewers.
Days 61–90: operate and improve
- Run approvals through the workflow rather than email-only decisions.
- Start monitoring the selected thresholds and record corrective actions.
- Schedule periodic and event-triggered reviews.
- Test one incident and one retirement scenario as a tabletop exercise.
- Decide whether the organization needs the additional management-system discipline of ISO/IEC 42001.
Keep legal readiness separate from voluntary governance
Maintain a legal register alongside the AI inventory. For each relevant jurisdiction, record the organization’s role, affected products and services, risk category, effective dates, documentation duties, transparency requirements, human-review requirements, and regulator or authority contacts. Have qualified counsel confirm the result for the actual facts.
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What a durable policy should contain
- Purpose, scope, definitions, and covered AI technologies.
- Roles, decision rights, training, communication, and escalation.
- Risk principles, prohibited uses, tiers, tolerances, and exceptions.
- Inventory and intake requirements for internal, purchased, embedded, and employee-used systems.
- Context and impact assessment requirements.
- Data, privacy, security, intellectual-property, accessibility, and rights controls.
- Testing, documentation, human oversight, user notice, and appeal or correction routes.
- Supplier diligence, contract terms, monitoring, incident response, and contingency plans.
- Records, review cadence, change triggers, metrics, and safe retirement.
- A legal-applicability process that is maintained separately from the voluntary framework.
The result is a policy that can begin small and become stricter as the organization learns. It creates evidence and accountability before a regulator, customer, employee, or affected person asks how an AI decision was made.
Frequently Asked Questions
Can a small organization use the NIST AI RMF without being required to?
Yes. NIST AI RMF 1.0 is designed as a voluntary, adaptable framework. Select the Govern, Map, Measure, and Manage practices that fit your uses and risk, rather than treating the Playbook as a mandatory checklist.
How often should an AI governance policy be reviewed?
There is no universal interval. Assign an owner and set a risk-based schedule, then require an additional review after material model or data changes, incidents, new deployment contexts, supplier changes, or changed legal obligations.
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No. It provides requirements and guidance for an AI management system and continual improvement. Legal compliance still depends on the organization’s jurisdiction, role, systems, uses, and applicable obligations.
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