LLM-Orchestrated Autonomous Data Governance: A Conversational Workflow for Policy Compliance and Dynamic Access Control

Current work, such as Zhou et al.’s tiered data sharing frameworks and Berry et al.’s integration of LLMs into clinical workflows, highlights the need for both strict governance and flexible, user-friendly interfaces. However, enforcing data governance and policies is still largely manual, rule-based, and inflexible, often lagging behind organizational changes. This research proposes leveraging the natural language reasoning and contextual awareness of LLMs to act as conversational “data stewards,” automating policy enforcement, generating audit trails, and dynamically adjusting access controls as workflows and regulations evolve. Imagine a system where users can interact in natural language to request access, clarify compliance requirements, or receive just-in-time explanations—while the LLM autonomously checks for HIPAA, GDPR, or internal policy compliance and adapts data-sharing permissions in real time. This goes beyond static rules (as in current frameworks) to proactive, explainable, and adaptive governance, potentially transforming both user experience and compliance robustness in data management workflows.

References:

1. Practice of Inter-departmental Collaboration Systems under a Tiered Data Sharing Framework and Integrated Application of Large Language Models. Lijie Zhou, Shoujun Yan, Zhongjie Li, Lifeng Song, Jie Ma, Yan Yang (2025). Youth Academic Annual Conference of Chinese Association of Automation.
2. Utilizing large language models for gastroenterology research: a conceptual framework. Parul Berry, R. Dhanakshirur, S. Khanna (2025). Therapeutic Advances in Gastroenterology.