The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
Alessio Buscemi, Thibault Simonetto, Daniele Pagani, German Castignani, Maxime Cordy, Jordi Cabot
TL;DR
The paper tackles the fragmentation of AI regulatory assessment by introducing the Sandbox Configurator, an open-source, modular framework that automates the creation of customised AI Evaluation & Testing Sandboxes (AITS) from a shared library of tests and dashboards. It formalises the AIRS lifecycle, differentiates Core AIRS (regulatory guidance) from Extended AIRS (technical testing), and maps 29 concrete activities to 15 regulatory-relevant requirements, aligning with the EU AI Act. A DSL-based configuration language and dynamic assembly mechanism enable interoperable, auditable sandbox configurations that can span national and cross-border contexts, with plug-in support for open-source and proprietary tests. The framework envisions a federated European ecosystem, interoperable across sectors and infrastructures (EDIHs, AI Factories, TEFs), to harmonise testing practices, accelerate regulator learning, and foster responsible innovation. The work provides illustrative journeys and outlines a concrete roadmap for implementation, inviting stakeholders to collaborate on a scalable, governance-aligned sandboxing infrastructure for trustworthy AI in Europe.
Abstract
The systematic assessment of AI systems is increasingly vital as these technologies enter high-stakes domains. To address this, the EU's Artificial Intelligence Act introduces AI Regulatory Sandboxes (AIRS): supervised environments where AI systems can be tested under the oversight of Competent Authorities (CAs), balancing innovation with compliance, particularly for startups and SMEs. Yet significant challenges remain: assessment methods are fragmented, tests lack standardisation, and feedback loops between developers and regulators are weak. To bridge these gaps, we propose the Sandbox Configurator, a modular open-source framework that enables users to select domain-relevant tests from a shared library and generate customised sandbox environments with integrated dashboards. Its plug-in architecture aims to support both open and proprietary modules, fostering a shared ecosystem of interoperable AI assessment services. The framework aims to address multiple stakeholders: CAs gain structured workflows for applying legal obligations; technical experts can integrate robust evaluation methods; and AI providers access a transparent pathway to compliance. By promoting cross-border collaboration and standardisation, the Sandbox Configurator's goal is to support a scalable and innovation-friendly European infrastructure for trustworthy AI governance.