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.
By Alessio Buscemi, Thibault Simonetto, Daniele Pagani, German Castignani, Maxime Cordy, Jordi Cabot
arXiv:2606. 01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrating artificial intelligence into citizen-facing applications such as chatbot assistants and eligibility assessments.
By Ahmet Kaplan
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.
By Ummara Mumtaz, Summaya Mumtaz
arXiv:2609.21192v1 Announce Type: new
Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
By John Cuneo, David Chun, Gaurav Khanna
arXiv:2606. 12423v1 Announce Type: cross Abstract: The rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks.
By Ayush Enkhtaivan, Chinazunwa Uwaoma
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%.
By Rudrendu Kumar Paul, Sourav Nandy
The paper introduces MARLA, a conceptual scaffold for regulatory learning under the EU AI Act, outlining a five‑stage cycle—Map, Assess, Report, Learn, Adapt—focused on implementing legal requirements into socio‑technical practices across local, national, and European levels. It emphasizes that regulatory learning must translate evidence from implementation into governance and legal knowledge to support consistent interpretation, effective oversight, and adaptation as technologies evolve. The scaffold is deliberately non‑prescriptive, offering a shared vocabulary for technical and legal stakeholders, and is illustrated through two pilot case studies and a prospective national‑to‑European illustration.
By Alessio Buscemi, Tom Deckenbrunnen, Imane Hmiddou, Marco Billi, Livio Rubino, Silvia Rizzuto Ferruzza, Daniele Pagani, Antonino Rotolo
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.
By Azmine Toushik Wasi, Mst Rafia Islam, Mahfuz Ahmed Anik, Taki Hasan Rafi, Md Manjurul Ahsan, Dong-Kyu Chae
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
By Sandeep Bokkasam, B. Durgalakshmi
The paper introduces the AI Assessment Sandbox Configurator, an open‑source framework designed to support technical assessment in AI Regulatory Sandboxes (AIRS) mandated by the EU Artificial Intelligence Act. It outlines 11 architectural and governance requirements for infrastructure that enables large‑scale, structured technical testing, and presents a catalogue of tests, a shared data model, dashboards, and reporting tools that harmonise heterogeneous outputs. An early‑stage pilot demonstrated the framework’s harmonisation and reporting capabilities within a live AIRS engagement, contributing to an official Exit Report.
By Alessio Buscemi, German Castignani, Daniele Pagani, Maxime Cordy, Jordi Cabot
arXiv:2609.36228v1 Announce Type: new
Abstract: The EU AI Act introduces extensive compliance requirements for organizations that develop, deploy, or integrate AI systems. Many of these requirements...
By Zhen Tao, Alize Kahraman, Shidong Pan, Zhenchang Xing, Chiara Ullstein, Jens Grossklags, Chunyang Chen
arXiv:2607. 21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging.
By Chris Reed, Alex Austria, Anmol Bharuka, Pragnitha Mandava, Khushiya Mujawar, Luka Shakhkulashvili