Permission-Aware RAG: Identity and Access Management (IAM)-Based Access Filtering in Multi-Resource Environments

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, which significantly improves response accuracy and contextual relevance. However, most existing RAG research implicitly assumes that all retrieved content is equally accessible to any user. This approach fails to address the complex, fine-grained access control required in enterprise environments in which data originates from multiple, independently governed sources. Attempting to merge heterogeneous authorization models, such as RBAC and ABAC, often leads to significant compliance burdens and unintended data exposure. To address this issue, we propose a permission-aware RAG framework that enforces resource-level access control by directly interfacing with provider-controlled Identity and Access Management (IAM) systems. By performing real-time permission validation against native IAM endpoints without requiring policy merging, this design preserves governance boundaries and ensures compliance. We implemented a prototype and conducted a comprehensive evaluation using a multi-hop QA benchmark. The evaluation included scenario-based qualitative analysis to verify policy enforcement, large-scale quantitative validation that confirms its scalable policy enforcement and quantifies the impact of access levels on answer quality. Results demonstrate that our framework provides secure, scalable information retrieval while maintaining strict adherence to diverse access policies across multi-resource environments.