Abstract
This publication discloses methods and systems for structurally separating components that analyse and propose from components that produce external effects within computational decision infrastructure. In one embodiment, a computational system enforces a structural separation between a determination-side partition (the analysis partition) and an effect-side partition (the execution partition) through its construction — such that no component in the analysis partition can directly invoke operations that produce external effects, regardless of that component’s credentials, permissions, or compromise status. The structural isolation is enforced by system architecture rather than by runtime policy or administrative configuration. In one embodiment, the separation applies with a zero exception path: no category of operation is exempt from the requirement that transitions between partitions present validated authorization derived from a completed determination, regardless of assessed consequence severity. The separation is actor-agnostic, applying identically to any computational or human-operated component operating within the analysis partition — computational processes, human-operated analysis tools, and automated pipelines all face the same structural constraint. In one embodiment, the structural separation provides passive compromise containment: if any analysis-partition component is compromised, the consequence scope is bounded to the analysis partition without requiring active detection. A compromised component may produce incorrect analysis and misleading proposals but cannot produce executed effects. The disclosed approaches may be enforced through multiple structural mechanisms including process isolation with no shared memory or direct invocation, network segmentation with no route from analysis to effect endpoints, interface-level separation where analysis interfaces exclude effect-producing operations, and per- tenant isolation in shared deployments. The structural separation establishes a privilege separation architecture and restricted execution environment applicable to AI agent containment, providing read-only analysis partition access and determination-derived authorization as the sole bridge from reasoning to execution. The structural separation relates to enterprise decision management architectures in that it governs the boundary between organisational determination and organisational action using confinement principles, and to AI safety infrastructure in that any computational component — including machine learning models operating as autonomous proposers — faces the same partition constraint, providing containment without relying on alignment techniques.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Winchester, Jayson, "Privilege Separation Architecture — Structural Confinement of Analysis Components in Decision Systems", Technical Disclosure Commons, (February 19, 2026)
https://www.tdcommons.org/dpubs_series/9357