AI-Driven Data Governance for Large Language Models: Ensuring Quality, Privacy, and Compliance Across Domains

Abstract

The rapid advancement of large language models (LLMs) such as GPT and BERT has revolutionized multiple industries, including healthcare, finance, supply chain management, and cybersecurity. While these models demonstrate remarkable capabilities in natural language understanding, content generation, and decision support, their performance, reliability, and ethical deployment are inherently dependent on robust data governance frameworks. This paper explores the critical role of AI-driven data governance in ensuring data quality, integrity, privacy, transparency, fairness, and regulatory compliance throughout the LLM lifecycle. Key components such as ethical AI standards, data lineage, traceability, and continuous model monitoring are examined as essential pillars for mitigating risks associated with data misuse, bias, hallucinations, and security breaches. The study further highlights domain-specific applications of AI data governance in sectors like healthcare, finance, cybersecurity, and supply chain management, illustrating how governance frameworks improve operational efficiency, regulatory adherence, and ethical decision-making. Challenges in implementing governance, including scalability, data complexity, transparency, and model monitoring, are also discussed. By emphasizing the integration of structured data management, privacy-preserving techniques, and regulatory compliance, this work provides a comprehensive overview of strategies to enhance trustworthiness, reliability, and accountability of LLMs. The findings underscore the importance of proactive governance approaches to ensure responsible, fair, and secure AI deployment in modern data-driven environments.