An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system.

Question

Options

• ARetention of stored data
• BPortability of data
• CData practices for training models
• DAccountability for data management

• A
  8% (2)
• B
  4% (1)
• C
  17% (4)
• D
  71% (17)

Explanation

Accountability for data management (D) is the most important consideration because it establishes who is responsible for ensuring data quality, integrity, compliance, and governance across the AI lifecycle. Without clear accountability, no other data governance practice can be reliably enforced or sustained-gaps in ownership lead to inconsistent standards that undermine trustworthiness. Data retention (A) is a policy concern but does not encompass the full breadth of governance. Data portability (B) addresses interoperability, not governance integrity. Data practices for training models (C) is important but is a subset of what accountability governs; accountability is the overarching control that ensures all practices-including training data management-are owned, monitored, and maintained.

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