AI-Driven Data Governance for Trustworthy Large Language Models: Challenges, Foundations, and Future Directions

Abstract

Large Language Models (LLMs) such as GPT-3, GPT-4, BERT, and their domain-specific variants have rapidly transformed software development and a wide range of application domains, including healthcare, finance, e-commerce, travel, cybersecurity, and education. These models demonstrate remarkable capabilities in understanding and generating human-like text, supporting decision-making, automating complex workflows, and processing massive volumes of structured and unstructured data. However, the performance, reliability, and trustworthiness of LLMs are fundamentally dependent on the quality, management, and governance of the data used throughout their lifecycle. Issues such as hallucinations, data misuse, biased outputs, privacy violations, security vulnerabilities, and regulatory non-compliance have emerged as critical challenges, limiting the safe deployment of LLMs in real-world, high-stakes environments. This work emphasizes the central role of AI-driven data governance as a foundational framework to address these challenges. It highlights how robust data governance practices—covering data quality, fairness, transparency, security, ethical compliance, and regulatory alignment—are essential for building reliable and accountable LLM systems. The paper discusses the impact of poor data practices on model performance and explores governance-driven solutions to mitigate risks such as data contamination, adversarial attacks, and ethical failures. Furthermore, it outlines key pillars, principles, and domain-specific applications of AI data governance, demonstrating its importance in enabling trustworthy, scalable, and compliant LLM deployment. Overall, the study positions AI-driven data governance as a critical enabler for sustainable and responsible advancement of large language models.