Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
- URL: http://arxiv.org/abs/2604.04712v1
- Date: Mon, 06 Apr 2026 14:26:14 GMT
- Title: Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
- Abstract summary: This paper bridges the gap between AI governance and computer engineering by proposing a taxonomy of 20 hardware-level governance mechanisms.<n>For each mechanism, we provide a technical description, a feasibility rating, and an identification of adversarial vulnerabilities.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The governance of frontier AI increasingly relies on controlling access to computational resources, yet the hardware-level mechanisms invoked by policy proposals remain largely unexamined from an engineering perspective. This paper bridges the gap between AI governance and computer engineering by proposing a taxonomy of 20 hardware-level governance mechanisms, organised by function (monitoring, verification, enforcement) and assessed for technical feasibility on a four-point scale from currently deployable to speculative. For each mechanism, we provide a technical description, a feasibility rating, and an identification of adversarial vulnerabilities. We map the taxonomy onto four governance scenarios: domestic regulation, bilateral agreements, multilateral treaty verification, and industry self-regulation. Our analysis reveals a structural mismatch: the mechanisms most needed for treaty verification, including on-chip compute metering, cryptographic proof-of-training, and hardware-embedded enforcement, are also the least mature. We assess principal threats to compute-based governance, including algorithmic efficiency gains, distributed training methods, and sovereignty concerns. We identify a temporal constraint: the window during which semiconductor manufacturing concentration makes hardware-level governance implementable is narrowing, while R&D timelines for critical mechanisms span years. We present an adversary-tiered threat analysis distinguishing commercial, non-state, and nation-state actors, arguing the appropriate security standard is tamper-evident assurance analogous to IAEA verification rather than absolute tamper-proofing. The taxonomy, feasibility classification, and mechanism-to-scenario mapping provide a technical foundation for policymakers and identify the R&D investments required before hardware-level governance can support verifiable international agreements.
Related papers
- Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance [0.0]
This paper asks which mechanisms are available once the regulatory object shifts from the training run to the inference call.<n>We develop a feasibility taxonomy of twenty inference-time mechanisms across monitoring, verification, and enforcement.<n>We then stress the taxonomy against a two-dimensional adversary model and map each mechanism to four governance scenarios.
arXiv Detail & Related papers (2026-09-09T12:37:17Z) - Near-Term Verification Methods for AI Chip Exports [0.0]
AI chip export controls can help the U.S. shape the development of frontier AI, but their effectiveness depends on reliable methods for verifying compliance.<n>This paper examines near-term verification mechanisms (implementable in approximately one year) and groups them into three categories.<n>We discuss how each mechanism can be implemented within the regulatory framework of the U.S. Bureau of Industry and Security.
arXiv Detail & Related papers (2026-09-07T15:30:11Z) - Provably Secure Agent Guardrail [89.79561918065122]
Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
arXiv Detail & Related papers (2026-05-28T02:12:41Z) - The Open-Weight Paradox: Why Restricting Access to AI Models May Undermine the Safety It Seeks to Protect [0.0]
The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety.<n>This paper argues that access restrictions, without governed alternatives, may displace risks rather than reduce them.
arXiv Detail & Related papers (2026-04-19T12:38:14Z) - AI Deployment Authorisation: A Global Standard for Machine-Readable Governance of High-Risk Artificial Intelligence [0.0]
This paper introduces the AI Deployment Authorisation Score (ADAS), a machine-readable regulatory framework that evaluates AI systems.<n>ADAS produces a cryptographically verifiable deployment certificate that regulators, insurers, and infrastructure operators can consume as a license to operate.
arXiv Detail & Related papers (2026-01-11T18:14:20Z) - Agentic AI for Financial Crime Compliance [0.0]
This paper presents the design and deployment of an agentic AI system for financial crime compliance (FCC) in digitally native financial platforms.<n>The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure under regulatory constraints.
arXiv Detail & Related papers (2025-09-16T14:53:51Z) - Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
arXiv Detail & Related papers (2025-09-08T17:52:08Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Toward a Global Regime for Compute Governance: Building the Pause Button [0.4952055253916912]
We propose a governance system designed to prevent AI systems from being trained by restricting access to computational resources.<n>We identify three key intervention points -- technical, traceability, and regulatory -- and organize them within a Governance--Enforcement--Verification framework.<n> Technical mechanisms include tamper-proof FLOP caps, model locking, and offline licensing.
arXiv Detail & Related papers (2025-06-25T15:18:19Z) - Control Tax: The Price of Keeping AI in Check [22.94651464417764]
We introduce the notion of Control tax -- the operational and financial cost of integrating control measures into AI pipelines.<n>We conduct comprehensive evaluations of state-of-the-art language models in adversarial settings.<n>We provide empirical financial cost estimates for control protocols and develop optimized monitoring strategies.
arXiv Detail & Related papers (2025-06-05T17:48:39Z) - Explainable AI Systems Must Be Contestable: Here's How to Make It Happen [2.5875936082584623]
This paper presents the first rigorous formal definition of contestability in explainable AI.<n>We introduce a modular framework of by-design and post-hoc mechanisms spanning human-centered interfaces, technical processes, and organizational architectures.<n>Our work equips practitioners with the tools to embed genuine recourse and accountability into AI systems.
arXiv Detail & Related papers (2025-06-02T13:32:05Z) - Watermarking Without Standards Is Not AI Governance [46.71493672772134]
We argue that current implementations risk serving as symbolic compliance rather than delivering effective oversight.<n>We propose a three-layer framework encompassing technical standards, audit infrastructure, and enforcement mechanisms.
arXiv Detail & Related papers (2025-05-27T18:10:04Z) - In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
We call for three interventions to advance system safety.<n>First, we propose using standardized AI flaw reports and rules of engagement for researchers.<n>Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs.<n>Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
arXiv Detail & Related papers (2025-03-21T05:09:46Z) - Open Problems in Technical AI Governance [102.19067750759471]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.<n>This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.