arXiv AI
An Empirical Study and Assessment of EU AI Act Compliance Checkers
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arXiv AI
Sep 18
Governance-as-Code: Translating EU AI Act Technical Requirements into Executable Compliance Pipelines for Generative AI Systems
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
arXiv AI
Sep 1
Operationalising AI Regulatory Sandboxes: Activities, Requirements, and Technical Assessment under the EU AI Act
The paper "Operationalising AI Regulatory Sandboxes: Activities, Requirements, and Technical Assessment under the EU AI Act" outlines a detailed framework for implementing AI Regulatory Sandboxes (AIRS) under the EU AI Act. It maps the sandbox lifecycle into 29 activities, distinguishes between a Core AIRS and an Extended AIRS that includes an AI Technical Sandbox (AITS), and derives 15 infrastructural and governance requirements linked to these activities and provider obligations. The authors also introduce the Sandbox Configurator, an open‑source tool to instantiate AITS environments, aiming to provide structured workflows for regulators, robust evaluation methods for experts, and a transparent compliance pathway for AI providers.
arXiv AI
Jul 7
From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline
arXiv:2607. 04448v1 Announce Type: cross Abstract: Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) poses a significant challenge, as requirements engineers must translate complex legal text into actionable software requirements - a process that remains largely manual and error-prone in practice.
arXiv AI
Aug 18
Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws
The paper argues that AI governance should rely on ISO-like interoperability protocols rather than solely on jurisdiction-specific laws. It proposes standardized AI nutrition labels that include metrics for bias, energy usage, and data provenance to enable machine‑readable risk communication across borders. These protocols aim to reduce regulatory fragmentation, lower barriers for SMEs, and build public trust while allowing modular evolution with technology.
arXiv AI
Jul 16
Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents
arXiv:2605. 16281v2 Announce Type: replace-cross Abstract: Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail.
arXiv AI
Sep 17
PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?
The paper introduces PACT, a benchmark designed to evaluate how well enterprise AI assistants follow compliance rules when faced with various pressures such as persistent users or hurried managers. PACT covers twelve regulated domains and forty-eight realistic multi‑turn scenarios, pairing each rule with a shortcut that violates it and applying different pressures across wording and system‑prompt modes. Using PACT, the authors profile six metrics of compliance and aggregate them into a PACTScore, revealing significant variability among 22 LLM models and that even top performers misapply rules 6–10% of the time, with user pressure increasing violations by 65% on average.