A Technical Policy Blueprint for Trustworthy Decentralized AI
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arXiv
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| Format: | Preprint |
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2025
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| author | Kassem, Hasan Banks, Orion Benjelloun, Omar Cansiz, Sergen Edwards, Brandon Foley, Patrick Hagestedt, Inken Jung, Taeho Kairouz, Peter Lorenzi, Marco Mattson, Peter Moorthy, Prakash Novakowski, Ann K O'Connor, Michael Rodrigues, Bruno Roth, Holger Sheller, Micah Stripelis, Dimitris Umeton, Renato Vesin, Marc Zhang, Wenbin Bowman, Mic Karargyris, Alexandros |
| author_facet | Kassem, Hasan Banks, Orion Benjelloun, Omar Cansiz, Sergen Edwards, Brandon Foley, Patrick Hagestedt, Inken Jung, Taeho Kairouz, Peter Lorenzi, Marco Mattson, Peter Moorthy, Prakash Novakowski, Ann K O'Connor, Michael Rodrigues, Bruno Roth, Holger Sheller, Micah Stripelis, Dimitris Umeton, Renato Vesin, Marc Zhang, Wenbin Bowman, Mic Karargyris, Alexandros |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Technical Policy Blueprint for Trustworthy Decentralized AI Kassem, Hasan Banks, Orion Benjelloun, Omar Cansiz, Sergen Edwards, Brandon Foley, Patrick Hagestedt, Inken Jung, Taeho Kairouz, Peter Lorenzi, Marco Mattson, Peter Moorthy, Prakash Novakowski, Ann K O'Connor, Michael Rodrigues, Bruno Roth, Holger Sheller, Micah Stripelis, Dimitris Umeton, Renato Vesin, Marc Zhang, Wenbin Bowman, Mic Karargyris, Alexandros Computers and Society Cryptography and Security 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. |
| title | A Technical Policy Blueprint for Trustworthy Decentralized AI |
| topic | Computers and Society Cryptography and Security |
| url | https://arxiv.org/abs/2512.11878 |