Preserving Decision Sovereignty in Military AI: A Trade-Secret-Safe Architectural Framework for Model Replaceability, Human Authority, and State Control
Peng Wei, Wesley Shu

TL;DR
This paper proposes a layered architectural framework for military AI that maintains decision sovereignty by ensuring model replaceability, human authority, and state control, even when using commercial AI models.
Contribution
It introduces a trade-secret-safe, model-agnostic architecture that preserves strategic decision authority and reduces dependency on proprietary models in military AI systems.
Findings
Defines decision sovereignty for military AI
Develops a threat model for boundary control
Proposes a layered architectural specification for model replaceability
Abstract
Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible: once a privately governed model becomes embedded in military workflows, the supplier can influence not only technical performance but also the operational boundary conditions under which the system may be used. This paper argues that the central strategic issue is not merely access to capable models, but preservation of decision sovereignty: the state's ability to retain authority over decision policy, version control, fallback behavior, auditability, and final action approval even when analytical modules are sourced from commercial vendors. Using the public Anthropic--Pentagon dispute of 2026, the broader history of Project Maven, and recent U.S., NATO, U.K., and intelligence-community guidance as a motivating context, the paper develops…
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