This work introduces the Guardrail Shadow Effect (GSE), a failure mode in Retrieval-Augmented Generation (RAG) systems where downstream safety and compliance layers unintentionally suppress the operational strength of grounded responses. While retrieval quality may remain high, excessive guardrail pressure can distort answer directness, dilute evidence utilization, and increase user friction. The paper proposes the Shadow Impact Score (SIS), a model-agnostic framework for detecting cross-layer interference between retrieval confidence, generation behavior, and safety activation pressure. Experimental scenarios across enterprise knowledge assistants, security workflows, and regulated environments demonstrate that systems can remain fully compliant while quietly degrading in practical usefulness. This work contributes to emerging research on second-order risks in aligned AI systems and provides an observability framework for maintaining proportional balance between safety posture and operational clarity in production RAG deployments.