Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

2026-03-03

AI Summary

This work addresses the lack of governance support—particularly for multi-party collaboration, controlled access, and traceable workflows—in existing AI experimentation environments. The authors propose and implement a governance-aware, multi-tenant AI sandbox platform based on a layered reference architecture that decouples presentation, control, execution, and data management layers. Integrated approval workflows and audit logging mechanisms structurally capture experimental context and governance decisions. The platform enables controlled onboarding, cross-project collaboration, and compliance verification, and for the first time facilitates the generation of reusable evaluation evidence, thereby enhancing experiment comparability and auditability. Its effectiveness has been validated in industrial–academic collaborative settings, yielding key insights into deployment and evolutionary practices for such governance-integrated platforms.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Hardware-aware ML
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

Collaborative AI experimentation in industry and academia requires environments that support rapid trials while maintaining controlled access, organisational isolation, and traceable workflows. Although interest in AI sandboxes is increasing, practical guidance on designing and building governance-aware experimentation platforms remains limited. This work designs and operationalizes a governance-aware, multi tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders. The sandbox was developed in an industry and academia ecosystem using iteratively validated requirements gathered from industrial partners. The solution adopts a layered reference architecture that separates a multi tenant presentation layer from a backend control plane and isolates execution and data management concerns into dedicated layers. The sandbox supports governed onboarding, project based collaboration, controlled access to AI services, and traceable experimentation through approval workflows and audit logging. By structuring experiment context and governance decisions as persistent records, the sandbox enables evaluation evidence to be reused and compared across projects and stakeholders. The development experience yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platforms.

Problem

• AI sandbox
• governance-aware
• multi-tenant
• collaborative experimentation
• traceable workflows

Innovation

• governance-aware AI
• multi-tenant sandbox
• traceable experimentation
• layered architecture
• reusable evaluation evidence