This whitepaper presents RAG Shield, a security-focused framework fordefending Retrieval-Augmented Generation (RAG) pipelines againstpoisoning and adversarial manipulation at the retrieval layer. The work introduces a multi-layer defense architecture combiningcryptographic document provenance validation, semantic anomaly detection,and secure, authority-weighted retrieval control. A realistic threatmodel is defined, focusing on poisoning of retrieval corpora rather thanprompt or model-level attacks. The system is evaluated against multipleattack scenarios under controlled conditions. RAG Shield is designed as a framework-agnostic security control layerthat operates independently of the underlying language model and vectordatabase, enabling deployment in enterprise and regulated environmentswithout modification of existing RAG architectures. This document is released as a technical preprint to establish prior artand support open discussion in the areas of AI security, adversarialmachine learning, and secure enterprise RAG deployment. Project website and system overview:https://sentinelrag.com Contact:info@sentinelrag.com