Assurance, audit, and insurance frameworks rely on the ability to observe, bound, and evidence risk. In systems mediated by probabilistic language models, a distinct assurance failure emerges: risk may form through AI-mediated judgment interaction without producing artefacts that are auditable, priceable, or certifiable. This paper identifies how AI-mediated language interaction creates an assurance gap upstream of identifiable loss or breach. Drawing on research in sociotechnical risk, audit theory, human–AI interaction, and AI evaluation limits, the paper demonstrates that existing assurance mechanisms remain structurally blind to this class of risk, not due to institutional failure, but due to misclassification of the assurance surface itself.