Data Governance in the Age of Artificial Intelligence: Frameworks, Ethics, and Compliance

Abstract

As artificial intelligence becomes increasingly integrated into organizational operations and decision-making processes, the importance of robust data governance has intensified significantly. This review examines the evolution of data governance frameworks in the context of AI adoption, analyzing how traditional governance structures must adapt to address the unique challenges posed by algorithmic decision-making systems. Drawing on literature from information systems, public administration, and AI ethics, we examine the core components of effective data governance, including data stewardship, quality management, compliance, and ethical oversight. We analyze the emerging regulatory landscape, including the EU AI Act, ISO/IEC 42001, and NIST's AI Risk Management Framework, and assess their implications for organizational governance practices. The review identifies key challenges in implementing AI-era data governance, including the tension between innovation and regulation, the difficulty of ensuring algorithmic accountability, and the need for continuous monitoring and adaptation. We propose a holistic governance framework that integrates technical, organizational, and ethical dimensions, providing practical guidance for practitioners and policymakers navigating the complex intersection of data governance and artificial intelligence.