AI Data Governance for Large Language Models: Frameworks, Best Practices, and Future Directions

Abstract

Large Language Models (LLMs) are rapidly transforming multiple sectors, including healthcare, finance, and cybersecurity, by enabling advanced data-driven insights and automation. However, the scale and complexity of LLMs introduce significant challenges in ensuring data privacy, ethical use, compliance with regulations, and mitigation of biases. AI data governance provides a structured approach to address these challenges by integrating robust frameworks, ethical guidelines, auditing mechanisms, and stakeholder collaboration throughout the LLM lifecycle. This article presents a comprehensive overview of AI data governance for LLMs, detailing critical components such as data collection, annotation, storage, management, usage, regulatory compliance, ethical frameworks, and accountability. It emphasizes best practices including the development of governance frameworks, leveraging AI-driven monitoring technologies, continuous improvement strategies, and human-in-the-loop collaboration to maintain data quality and trustworthiness. The study also examines real-world implementations in enterprises, showcasing case studies from industries like finance, telecom, and cloud services, highlighting the integration of frameworks such as IBM watsonx.governance and blockchain-based traceability approaches. Additionally, the article identifies open challenges, including scalability, cross-border compliance, security risks, data provenance, and bias mitigation, and suggests future research directions to create standardized, adaptive, and transparent governance systems. Overall, this work underscores the importance of a holistic, ethical, and regulatory-aware approach to AI data governance to ensure responsible, secure, and trustworthy deployment of LLMs across diverse domains.