Privacy-Preserving Retrieval over Enterprise Databases for RAG Systems: A Systematic Review and Research Agenda

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Retrieval-augmented generation (RAG) is increasingly used in enterprise environments to support large language models with access to internal databases and knowledge sources. However, such integration introduces privacy risks related to retrieval leakage, prompt exposure, embedding leakage, and cross-boundary data disclosure. This paper presents a systematic review of privacy-preserving retrieval approaches for enterprise RAG systems. The analysis covers cryptographic methods, differential privacy, confidential computing, and hybrid protection architectures. A taxonomy and comparative perspective are provided, together with key research challenges and future directions. The findings show that layered and adaptive protection architectures offer the most promising path for privacy-aware enterprise RAG deployment.