Decentralized AI systems, such as federated learning, can play a critical role in further unlocking AI asset marketplaces (e.g., healthcare data marketplaces) thanks to increased asset privacy protection. Unlocking this big potential necessitates governance mechanisms that are transparent, scalable, and verifiable. However current governance approaches rely on bespoke, infrastructure-specific policies that hinder asset interoperability and trust among systems. We are proposing a Technical Policy Blueprint that encodes governance requirements as policy-as-code objects and separates asset policy verification from asset policy enforcement. In this architecture the Policy Engine verifies evidence (e.g., identities, signatures, payments, trusted-hardware attestations) and issues capability packages. Asset Guardians (e.g. data guardians, model guardians, computation guardians, etc.) enforce access or execution solely based on these capability packages. This core concept of decoupling policy processing from capabilities enables governance to evolve without reconfiguring AI infrastructure, thus creating an approach that is transparent, auditable, and resilient to change.

翻译：去中心化人工智能系统（如联邦学习）因增强了资产隐私保护，可在进一步释放AI资产市场（如医疗数据市场）潜力方面发挥关键作用。释放这一巨大潜力需要透明、可扩展且可验证的治理机制。然而，当前的治理方法依赖于定制化、特定于基础设施的策略，这些策略阻碍了资产互操作性和系统间的信任。我们提出了一种技术政策蓝图，将治理需求编码为策略即代码对象，并将资产策略验证与资产策略执行分离。在此架构中，策略引擎验证证据（如身份、签名、支付、可信硬件证明）并下发能力包。资产守护者（例如数据守护者、模型守护者、计算守护者等）仅根据这些能力包来执行访问或计算。这种将策略处理与能力解耦的核心概念，使得治理能够在不重新配置AI基础设施的情况下演进，从而创造一种透明、可审计且能够适应变化的方案。