Abstract: Retrieval-augmented generation (RAG) is an emerging AI framework that enhances the capabilities of generative language models by integrating external retrieval mechanisms, enabling them to produce contextually relevant and factually grounded responses. This hybrid approach combines the precision of retrieval systems with the generative richness of advanced transformer models, reducing hallucinations and making RAG ideal for applications such as question-answering, knowledge management, and customer service automation. However, the unique architecture of RAG systems introduces critical security challenges, data privacy risks, model poisoning, inference attacks, and unauthorized access.
This paper provides a comprehensive analysis of RAG systems, starting with a definition of their core architecture and a detailed exploration of the frameworks that constitute RAG, such as LangChain and Haystack[1]. We then identify common architectural patterns, such as pipeline and cascade architectures, and discuss the supporting systems that underpin RAG functionality, including vector stores and orchestration layers. Building upon this foundation, we analyze the security threats faced by RAG frameworks and offer practical recommendations to mitigate these risks. Key strategies include implementing data access controls, secure communication protocols, model integrity checks, and rigorous data labeling and training processes.
By integrating security measures directly into the design and deployment of RAG systems, this paper outlines a secure framework that balances functionality and protection. The proposed framework provides actionable insights for developers and organizations aiming to deploy RAG applications in sensitive and dynamic environments while safeguarding data and ensuring compliance.
Keywords: Retrieval-augmented generation (RAG), AI Security, Secure Framework, Building Security in AI.
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DOI:
10.17148/IJARCCE.2025.14114
