Self-updating Retrieval Mechanisms for Scalable and Personalized RAG Systems in Dynamic Environments

Retrieval-augmented generation (RAG) has become a practical mechanism for improving the factuality, traceability, and domain relevance of large language model (LLM) systems. However, many deployed RAG pipelines remain static: documents are embedded periodically, user context is handled through shallow prompt engineering, and compliance controls are applied after retrieval rather than during inference. These assumptions are increasingly inadequate for healthcare, finance, insurance, and regulated enterprise environments where documents, policies, user permissions, and operational risk profiles change continuously. This paper proposes SURA-RAG , a self-updating retrieval architecture for scalable and personalized RAG systems in dynamic environments. The framework combines event-driven indexing, freshness-aware ranking, edge-cloud LLM orchestration, federated personalization signals, policy-gated retrieval, and explainable evidence logging. A lightweight simulation is presented to compare static RAG, periodic index rebuilding, cloud-only personalization, and the proposed self-updating architecture. Results indicate that SURA-RAG can improve retrieval freshness, reduce stale-answer risk, and strengthen compliance traceability while maintaining acceptable latency. The paper contributes a concise technical framework for privacy-preserving, scalable, and auditable RAG deployment in high-impact sectors.