AI Data Governance Frameworks for Large Language Models: Supply Chain Management and Domain-Specific Applications

Abstract

Artificial Intelligence (AI) data governance has become a critical component in ensuring the responsible, secure, and transparent deployment of AI systems across multiple domains. This article explores the implementation of AI data governance in supply chain management, healthcare, cybersecurity, and finance, highlighting how governance frameworks improve compliance, accountability, data integrity, privacy, and operational efficiency. The study further examines domain-specific governance strategies for Large Language Models (LLMs), emphasizing the need for tailored approaches that address sector-specific regulatory and ethical requirements. In addition, the article discusses major challenges associated with data governance for LLMs, including data quality and bias, privacy and security risks, transparency and explainability limitations, and scalability and complexity issues. The analysis reveals that although LLMs provide significant opportunities for automation, intelligent decision-making, and enhanced operational performance, their large-scale deployment introduces concerns related to ethical AI use, regulatory compliance, data ownership, and infrastructure costs. The article concludes that robust AI data governance frameworks are essential for building trustworthy, fair, and accountable AI systems while ensuring sustainable and secure integration of LLMs across diverse industries.