A modern AI governance program defines risk ownership as a shared responsibility across the organization, with clearly mapped decision rights. Instead of a single department “owning AI,” you create a system where each stakeholder has defined roles across the AI lifecycle—from idea to retirement.​

In practice, a shared AI governance model typically includes:

• Board and Executive Leadership
  Sets overall risk appetite for AI, approves critical policies, and ensures AI risk is integrated into enterprise risk management.​

• Business Owners (e.g., Operations, Product, Marketing, Customer Experience)
  Own use‑case selection, business outcomes, and alignment with customer expectations and brand values.​

• IT & Security
  Own infrastructure, access, technical controls, monitoring, and incident response for AI systems.​

• Legal, Compliance, and Privacy
  Interpret and apply laws and regulations (EU AI Act, GDPR, CCPA/CPRA, sector‑specific rules), guide contract clauses, DPIAs, and policy boundaries.​

• Risk & Internal Audit
  Independently assess the effectiveness of AI controls and provide assurance to the board.​

A Stakeholder Accountability Map (often in RACI format) makes this explicit: for each AI initiative, you can see who is Responsible, Accountable, Consulted, and Informed at every phase.​