Non-Invertible External Supervisory Control: A Theoretical and Architectural Framework for External Supervision and Explicit Operational Risk Management in Large-Scale Artificial Intelligence Systems

This preprint introduces the Non-Invertible External Supervisory Control (NIESC/CSENI) framework, a theoretical and architectural proposal aimed at examining the structural limitations of AI safety approaches based exclusively on internal alignment. The paper argues that, under conditions of model opacity, limited auditability, and strategic capability of the supervised system, safety cannot rely solely on restrictions embedded within the same computational substrate as the agent. CSENI is formulated as an external supervisory layer centered on three elements: controller decoupling, gradual operational friction, and heterogeneous observables. The paper includes a preliminary threat model, a minimal formalization of the supervisor, an initial experimental agenda, and a technical appendix with a reproducible minimal instantiation for tool-using agents. This work does not claim to offer a definitive solution to the control problem for advanced AI systems. Its purpose is foundational: to propose a conceptual vocabulary, a minimal formal basis, and a forward-looking program for future technical, regulatory, and institutional developments.