Practice of Inter-departmental Collaboration Systems under a Tiered Data Sharing Framework and Integrated Application of Large Language Models

In the era of digital transformation, data-driven collaboration has become pivotal for organizational efficiency and innovation. This paper proposes a tiered data sharing framework to address data silos and security challenges in cross-departmental workflows. The framework integrates a triple-role management model and dynamic data classification, ensuring secure and adaptive data governance. Building upon this framework, This paper designs a five-layer architecture for an inter-departmental collaboration system, enabling scalable data integration, business support, and user-centric services. Furthermore, This paper pioneers the integration of large language models (LLMs) into collaboration workflows, achieving AI-augmented document processing, context-aware access control, and predictive resource allocation. Through practical implementation, the system demonstrates significant improvements in collaboration efficiency, security, and decision-making agility. By synthesizing short-term operational insights with long-term strategic foresight, this study provides actionable strategies for organizations navigating digital transformation. (Abstract)