Large-scale artificial intelligence (AI) systems rely extensively on vast amounts of data, sophisticated computational architectures, and complex multi-stakeholder ecosystems. As AI becomes deeply embedded into enterprise workflows, government functions, and societal infrastructures, effective data governance becomes essential for ensuring ethical integrity, accuracy, fairness, transparency, and operational reliability. This paper provides a conceptual review of data governance frameworks for large-scale AI systems. It integrates insights from data management theory, AI ethics, socio-technical systems, and risk governance literature to develop a comprehensive Data Governance Framework for Large-Scale AI Systems (DGF-AI). The framework categorizes governance into six domains—data quality, data privacy, data security, data lineage, data accountability, and AI model governance—illustrated with conceptual diagrams. The paper concludes with implications for policymakers, enterprises, and AI researchers.