A Technical Policy Blueprint for Trustworthy Decentralized AI

TL;DR

This paper proposes a modular, transparent governance framework for decentralized AI systems that separates policy verification from enforcement, enhancing trust, scalability, and adaptability in AI asset marketplaces.

Contribution

It introduces a policy-as-code architecture that decouples policy verification from enforcement, enabling flexible, auditable governance for decentralized AI systems.

Findings

Policy verification is separated from enforcement, improving flexibility.

The architecture supports transparent and auditable governance.

Decoupling enables governance evolution without infrastructure reconfiguration.

Abstract

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.