Executive sponsor
Sets governance expectations, approves risk appetite, resolves escalated decisions, and receives executive reporting.
AI governance guide
AI Governance Framework Example
A practical example of how an organization can structure AI ownership, inventory, risk classification, approvals, human oversight, monitoring, and evidence.
Example operating model
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One practical way to structure AI governance.
The exact model should be adapted to organization size, AI maturity, regulatory exposure, and existing cybersecurity, privacy, legal, procurement, and GRC processes.
AI governance committee
Coordinates security, privacy, legal, risk, data, procurement, technology, and business stakeholders.
AI inventory
Records each AI use case, owner, vendor/model, purpose, users, data, integrations, and lifecycle status.
Risk tiering
Classifies use cases according to impact, autonomy, sensitive data, affected stakeholders, and business criticality.
Risk review
Higher-risk use cases receive formal assessment, control requirements, testing, approvals, and evidence.
Human oversight
Defines when people must review, approve, challenge, or override AI-generated recommendations or actions.
Third-party review
Evaluates vendor AI, data use, retention, model training, subprocessors, contractual obligations, and security posture.
Monitoring & evidence
Tracks changes, incidents, exceptions, reviews, control evidence, performance, and governance metrics.
Example governance workflow
Business owner submits AI use case → inventory entry created → initial risk tier assigned → required reviewers engaged → controls and human oversight defined → approval or remediation → evidence retained → periodic review and monitoring.
What the framework should produce
Governance becomes useful when it produces clear decisions and evidence.
Clear ownership
Every material AI use case has a business owner and defined governance responsibilities.
Consistent risk decisions
Similar AI use cases are reviewed using repeatable criteria instead of ad hoc judgment.
Human accountability
Approval and oversight requirements are explicit for higher-impact or more autonomous AI use.
Audit-ready evidence
Approvals, risk decisions, controls, exceptions, reviews, and monitoring are retained as evidence.
AI governance
Need help adapting this example to your organization?
We can help define the operating model, roles, risk tiers, policies, approval workflow, evidence, and implementation roadmap.