AI Governance Policy: What Companies Should Include
Compliance & Information Security

What Should Be Included in an AI Governance Policy?

An AI governance policy should do more than say the company will use artificial intelligence responsibly. It should define who is accountable, what uses are permitted, how risk is assessed, what controls are required, and how AI systems are monitored over time. In practice, a strong policy connects legal, security, privacy, operational, and ethical requirements into one governance structure.

Core Elements Policy Outline Implementation Notes External Resources

Core elements of an AI governance policy

The policy should define the organization’s position on AI use, assign decision rights, and establish control requirements across the AI lifecycle, from procurement and development through deployment, monitoring, and retirement.

1. Purpose, scope, and definitions

Define what the policy covers, including generative AI tools, embedded AI features in third-party software, internally developed models, machine learning systems, and automated decision support. Include plain-language definitions so business users and technical teams interpret the policy consistently.

2. Governance structure and accountability

Identify who approves AI use cases, who performs risk review, who owns ongoing monitoring, and which functions must be involved, such as legal, security, privacy, compliance, HR, procurement, and business leadership. A policy without named accountability is usually only advisory.

3. AI inventory and use-case classification

Require a central inventory of AI systems and use cases. Classify them by risk level, business criticality, data sensitivity, external impact, and degree of autonomy. This helps separate low-risk productivity use from high-risk decision support or customer-facing automation.

4. Acceptable use and prohibited use

State what employees may do, what requires approval, and what is prohibited. For example, a company may permit low-risk drafting assistance but prohibit entering regulated, privileged, export-controlled, trade secret, or customer confidential data into unapproved public AI tools.

5. Risk assessment and approval workflow

Require documented review before deploying or materially changing an AI use case. The review should consider accuracy, security, privacy, bias, explainability, resilience, vendor dependency, legal exposure, and the consequences of incorrect outputs.

6. Data governance and privacy controls

Address data minimization, data lineage, retention, consent, lawful basis, cross-border transfer, de-identification, training-data restrictions, prompt handling, and whether user data may be used by a provider to train its models.

7. Security requirements

Establish baseline controls such as access management, logging, encryption, secure API usage, model and prompt protection, adversarial testing where appropriate, and review of provider security posture. AI governance should connect directly to the organization’s broader information security program.

8. Human oversight and decision authority

Clarify when a human must review outputs before action is taken. This matters most when AI could affect customers, employees, applicants, safety, financial commitments, investigations, or regulated decisions.

9. Transparency and disclosure

Define when the company must disclose AI use internally or externally. This may include disclosures to employees, customers, regulators, or counterparties, especially when AI materially contributes to content generation, recommendations, or automated decisions.

10. Testing, validation, and performance monitoring

Require testing before launch and periodic reevaluation after deployment. Performance should not be assumed to remain stable. The policy should address quality thresholds, drift, hallucination risk, fallback procedures, and escalation triggers when performance degrades.

11. Vendor management and procurement controls

Require due diligence for third-party AI tools and embedded AI features. Contracts should address security, data use, audit rights where feasible, confidentiality, retention, subcontractors, model changes, and incident notification.

12. Incident management and issue escalation

Define what qualifies as an AI-related incident, such as harmful output, privacy exposure, unauthorized use, model failure, or significant bias concerns. State who must be notified, how evidence is retained, and when use must be suspended.

13. Legal and regulatory alignment

The policy should acknowledge that AI obligations may arise from privacy law, sector rules, employment law, consumer protection, cybersecurity obligations, contractual commitments, and jurisdiction-specific AI regulation.

14. Training and user responsibilities

Employees need practical instruction, not abstract principles. The policy should require role-based training, explain approved tools, warn against overreliance on outputs, and make clear that users remain responsible for the decisions they make with AI assistance.

15. Documentation, auditability, and records retention

Require records of approvals, risk assessments, testing, incidents, exceptions, and periodic reviews. Mature governance depends on evidence, especially when auditors, customers, regulators, or insurers ask how AI use is controlled.

16. Review cadence and continual improvement

AI governance should be reviewed on a defined schedule and after major legal, technical, or operational changes. A static policy will become outdated quickly as tools, regulations, and business uses evolve.

A useful way to think about policy design is this: the document should not merely express principles. It should create operational controls, approval gates, and evidence that governance is actually happening.

Suggested policy outline

  1. Purpose
  2. Scope
  3. Definitions
  4. Governance roles and responsibilities
  5. AI use-case inventory and classification
  6. Acceptable and prohibited use
  7. Risk assessment and approval requirements
  8. Data governance, privacy, and confidentiality
  9. Security controls for AI systems and tools
  10. Human oversight and review requirements
  11. Testing, validation, and change management
  12. Transparency and disclosure obligations
  13. Third-party and vendor management
  14. Monitoring, logging, and performance review
  15. Incident response and escalation
  16. Training and awareness
  17. Exceptions process
  18. Recordkeeping and audit support
  19. Policy violations and enforcement
  20. Review cycle and ownership

Implementation notes for companies

Start with use cases, not slogans

Many organizations begin with broad statements about “responsible AI,” but governance becomes effective only when tied to actual use cases, data flows, vendors, and decision points.

Differentiate low-risk from high-risk AI

Internal drafting assistance and summarization tools do not create the same exposure as AI used in hiring, customer eligibility, fraud analysis, security operations, health-related decisions, or legal workflows.

Connect AI governance to existing control frameworks

AI governance should align with privacy, security, vendor risk management, records management, and incident response processes already in place. That reduces duplication and makes the policy easier to operationalize.

Review third-party tools carefully

Risk often enters through SaaS platforms that quietly add AI features. Procurement, security, and legal review should address what the feature does, what data it receives, and how outputs can be relied upon.

External resources on AI governance

These sources are useful starting points for building or refining an internal AI governance policy.

NIST AI RMF Resource Center

Supporting materials, profiles, and implementation resources that help translate the framework into operating practice.

OECD AI Principles

International principles focused on trustworthy AI, human rights, transparency, accountability, robustness, and policy alignment across jurisdictions.

ISO/IEC 42001: AI Management Systems

The first AI management system standard, useful for organizations that want governance to sit in a structured, auditable management system model.

ISO 42001 Explained

A more accessible overview of the standard and the governance elements it expects organizations to address.

European Commission: AI Act Overview

An official summary of the EU AI Act and its risk-based regulatory model, which is increasingly influential even for companies outside the EU.

This page is informational and should not be treated as legal advice. Companies should adapt AI governance requirements to their industry, risk profile, jurisdictions, customer commitments, and existing compliance obligations.