Which roles are commonly involved in AI governance?

• A. AI steering committees, CISOs, data owners, compliance officers, and business unit leaders.
• B. End users and customers.
• C. Data scientists, developers, and QA testers.
• D. Marketing strategists and sales teams.

Answer: (A)

AI governance relies on cross-functional oversight to manage risk, ensure compliance, protect data, and keep AI initiatives aligned with business goals. The best answer reflects a governance setup that brings together strategic direction, security, data stewardship, regulatory compliance, and business accountability.

An AI steering committee provides the overall direction, policy setting, and governance framework, guiding how AI is used across the organization. The CISO focuses on information security and risk management, ensuring AI systems are protected from threats and correctly secured. Data owners are responsible for data quality, access, and stewardship, which is crucial for trustworthy AI and proper data governance. Compliance officers ensure the organization follows relevant laws, regulations, and internal policies, helping to manage legal and ethical risks. Business unit leaders ensure accountability and that AI initiatives deliver real value and align with strategic objectives.

End users and customers influence requirements and feedback, but they aren’t governance bodies that establish policies or oversight. Data scientists, developers, and QA testers build and test AI systems; they operate within the governance framework rather than governing it. Marketing strategists and sales teams shape how AI outcomes are used in market-facing activities but aren’t the governance structure themselves.

So, the combination of strategic governance, security, data stewardship, compliance, and business accountability roles best captures who is commonly involved in AI governance.