Frontier artificial intelligence systems increasingly operate within legal, institutional, and safety-critical environments where generated outputs may become transferable, reproducible, and legally discoverable artifacts. Recent developments across litigation practice, model distillation research, and AI deployment contexts reveal a shared structural condition: once inference has occurred, governance mechanisms can only react to consequences rather than prevent exposure. This publication introduces Pre-Inference Authorization as an architectural governance principle addressing inference itself as the primary control boundary. Rather than treating artificial intelligence outputs as objects requiring downstream regulation, the paper reframes inference as a conditionally authorizable execution event. Under this model, epistemic exposure — including unintended disclosure, capability extraction, evidentiary persistence, or uncontrolled replication — originates from execution occurring prior to legitimacy verification. Using the A7SEM (Akarkach 7-Stage Emergence Model) as analytical framework, the study demonstrates why post-hoc monitoring, filtering, or contractual restriction cannot fully mitigate risks once inference has produced transferable artifacts. The proposed architecture introduces authorization-before-execution as a stabilizing boundary enabling controllable, auditable, and legally governable AI operation without prescribing implementation mechanisms or regulatory mandates. This work provides architectural clarification intended for governance researchers, legal practitioners, insurers, and AI system designers. No legal claims, compliance assertions, or operational instructions are made. CITATION BLOCK Akarkach, M. (2026).Preventing Epistemic Exposure in Frontier Artificial Intelligence Systems:Pre-Inference Authorization as an Architectural Solution to Inference Extraction and Discovery Risk.Zenodo. https://doi.org/10.5281/zenodo.18795429