The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
- URL: http://arxiv.org/abs/2509.25256v3
- Date: Wed, 08 Oct 2025 19:27:18 GMT
- Title: The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
- Abstract summary: 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)<n>Yet significant challenges remain: assessment methods are fragmented, tests lack standardisation, and feedback loops between developers and regulators are weak.<n>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.
- Score: 13.108419500875543
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges [0.0]
This survey delivers a comprehensive systems-security reference framework for trustworthy agentic AI.<n>We formalize the general agent architecture as a stateful 5-tuple and establish a 6-dimensional trustworthiness taxonomy covering security, safety, privacy, explainability, fairness, and accountability.
arXiv Detail & Related papers (2026-09-12T05:51:27Z) - Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
arXiv Detail & Related papers (2026-01-16T18:44:09Z) - Trustworthy Orchestration Artificial Intelligence by the Ten Criteria with Control-Plane Governance [1.9691447018712314]
This paper presents the Ten Criteria for Trustworthy Orchestration AI.<n>It integrates human input, semantic coherence, audit and provenance integrity into a unified Control-Panel architecture.
arXiv Detail & Related papers (2025-12-11T05:49:26Z) - TAIBOM: Bringing Trustworthiness to AI-Enabled Systems [0.23332469289621785]
Software Bills of Materials (SBOMs) have become critical for enhancing transparency and traceability.<n>Current frameworks fall short in capturing the unique characteristics of AI systems.<n>We introduce Trusted AI Bill of Materials (TAIBOM) -- a novel framework extending SBOM principles to the AI domain.
arXiv Detail & Related papers (2025-10-02T16:17:07Z) - Using the NANDA Index Architecture in Practice: An Enterprise Perspective [9.707223291705601]
The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems.<n>This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems.
arXiv Detail & Related papers (2025-08-05T05:27:27Z) - Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
arXiv Detail & Related papers (2025-08-01T08:11:31Z) - 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) - Enabling External Scrutiny of AI Systems with Privacy-Enhancing Technologies [0.0]
This article describes how technical infrastructure developed by the nonprofit OpenMined enables external scrutiny of AI systems without compromising sensitive information.<n>In practice, external researchers have struggled to gain access to AI systems because of AI companies' legitimate concerns about security, privacy, and intellectual property.<n>PETs have reached a new level of maturity: end-to-end technical infrastructure developed by OpenMined combines several PETs into various setups that enable privacy-preserving audits of AI systems.
arXiv Detail & Related papers (2025-02-05T15:31:11Z) - How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception [4.075971633195745]
Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles.
The European Union (EU) Artificial Intelligence (AI) Act aims to address these challenges by establishing stringent norms and standards for AI systems.
This review paper summarizes the requirements arising from the EU AI Act regarding DNN-based perception systems and systematically categorizes existing generative AI applications in AD.
arXiv Detail & Related papers (2024-08-30T12:01:06Z) - Developing an AI-enabled IIoT platform -- Lessons learned from early use
case validation [47.37985501848305]
We introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection.
This is complemented by insights and lessons learned during this early evaluation activity.
arXiv Detail & Related papers (2022-07-10T18:51:12Z) - Multisource AI Scorecard Table for System Evaluation [3.74397577716445]
The paper describes a Multisource AI Scorecard Table (MAST) that provides the developer and user of an artificial intelligence (AI)/machine learning (ML) system with a standard checklist.
The paper explores how the analytic tradecraft standards outlined in Intelligence Community Directive (ICD) 203 can provide a framework for assessing the performance of an AI system.
arXiv Detail & Related papers (2021-02-08T03:37:40Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
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.