Intelligent AI Data Governance Frameworks for Large Language Models: Challenges, Applications, and Future Perspectives

Abstract

The rapid advancement of Large Language Models (LLMs) has significantly transformed industries such as healthcare, finance, e-commerce, travel, and cybersecurity by enabling intelligent automation, advanced analytics, and human-like communication. However, the growing dependence on LLMs has introduced critical challenges related to data misuse, hallucinations, bias, privacy violations, security threats, and ethical concerns. This article examines the importance of AI data governance frameworks in ensuring secure, ethical, transparent, and reliable LLM deployment throughout the AI lifecycle. It discusses the role of data governance in improving data quality management, model fine-tuning, privacy protection, regulatory compliance, and operational efficiency while minimizing risks associated with misinformation, adversarial attacks, and deployment failures. The study further explores how governance mechanisms support trustworthy AI systems in healthcare, finance, e-commerce, travel, and other domains. Additionally, the article highlights major governance challenges and emphasizes the need for intelligent, data-centric governance architectures capable of maintaining fairness, accountability, security, and compliance in evolving AI ecosystems. The study concludes that robust AI data governance frameworks are essential for building trustworthy and sustainable LLM-based systems across modern digital industries.