Research on Data Security Protection Strategies for Enterprise-Level Large Model RAG Systems
波 杨 · Hans Journal of Data Mining · 2025
检索增强生成(Retrieval-Augmented Generation, RAG)系统作为企业数字化转型与大模型技术结合的典型应用形式，为企业提供精准信息服务的同时，也带来了严峻的数据安全挑战。本文首先深度分析了RAG系统应用在数据全生命周期各阶段面临的数据泄露、数据篡改、数据滥用等数据安全风险；其次针对各阶段数据安全风险制定了具体的数据安全防护策略，以及整体数据安全管理措施；最后讨论了研究的局限性及未来研究方向的展望。本文旨在为企业构建全面、有效的企业级大模型RAG系统数据安全防护策略提供理论指导和实践参考，以保障企业RAG应用健康发展。Retrieval-Augmented Generation (RAG) systems, as a paradigmatic application integrating enterprise digital transformation with large model technologies, deliver precise information services to businesses while introducing critical data security challenges. This paper first conducts an in-depth analysis of data security risks—including data leakage, data tampering, and data misuse—across the entire lifecycle of RAG system applications. Subsequently, it formulates specific data security protection strategies for each stage and holistic data security management measures. Finally, it discusses the limitations of the study and prospects for future research directions. The objective of this paper is to furnish theoretical guidance and practical references for enterprises to build comprehensive and effective data security protection strategies for enterprise-level large model RAG systems, to ensure the healthy development of RAG applications in enterprises.