Decentralized AI Governance Must Decouple Policy Processing from Capability Enforcement

In this position paper, we argue that successful decentralized AI needs trustworthy, adaptive, and portable governance at scale. The ML community must adopt an architecture that cleanly separates \emph{policy processing} (the evaluation of evidence against governance requirements) from \emph{capability enforcement} (the gating of access to protected digital assets). We argue that the prevailing approach, in which each organization manages bespoke policy logic, custom-built for its infrastructure, produces fragmentation that undermines interoperability, transparency, and auditability of federated systems. Drawing on a survey of governance mechanisms in federated learning and data collaboration frameworks and on a concrete reference architecture built around community-driven policy objects, we argue that this decoupling is technically feasible and architecturally necessary and supports a broad class of decentralized AI governance settings. We further suggest that the AI community should invest in open, standardized policy abstractions rather than proliferate siloed governance solutions. We address counterarguments concerning the costs of standardization and the feasibility of universal policy languages.