The Importance of AI Data Governance in Large Language Models

AI data governance is a crucial framework for ensuring that data is utilized in the life cycle of large language models (LLMs) activity from the development process, end-to-end testing process, model validation, secure deployment, and operations. This requires being managed responsibly, confidentially, securely, and ethically. The main objective of data governance is to implement the robust and intelligent data governance framework for LLMs which tends to impact on data quality management, fine-tuning of model performance, biases, data privacy laws, security protocols, ethical AI practices, and regulatory compliance process in LLMs. Effective data governance steps are important for minimizing data breach activity, enhancing data security, ensuring compliance and regulations, mitigating bias, and establishing clear policies and guidelines. This paper covers the foundation of AI data governance, key components, types of data governance, best practices, case studies, challenges, and future directions of data governance in LLMs. Additionally, we conduct a comprehensive detailed analysis of data governance and how efficient the integration of AI data governance is needed for LLMs to gain a trustable approach for the end user. Finally, we provide deeper insights into the comprehensive exploration of the relevance of the data governance framework to the current landscape of LLMs in the healthcare, pharmaceutical, finance, supply chain management, and cybersecurity sectors and address the essential roles to take advantage of the approach of data governance frameworks and their effectiveness and limitations.