Bounded Sovereignty: Controlling AI When You Don't Own the Model.

Many AI safety protocols assume full control over a model and its infrastructure, which is often not the case for organizations using third-party frontier AI models through APIs. This research introduces the concept of "bounded sovereignty," defining it as the limited technical and contractual access an organization has across various layers of the AI stack, including data, model, infrastructure, and interaction logs. The paper argues that these access conditions directly determine which AI control protocols can be effectively implemented. It provides a four-layer access typology and a matrix mapping control protocols to their access requirements. Furthermore, it introduces the "sovereignty discount cost," which represents the resources spent (e.g., contracts, audits, reduced scope) to compensate for insufficient access and maintain safety. Experimental simulations demonstrate that comprehensive logs, pre-execution gateways, and trace access significantly improve safety and diagnostic capabilities, while scope restriction can enhance safety at the cost of utility.