Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned
This work provides practical guidance for building governance-aware AI experimentation platforms, addressing a gap in design and implementation for collaborative environments.
The paper designs and implements a governance-aware, multi-tenant AI sandbox for industry-academia collaboration, enabling structured experimentation with controlled access, organizational isolation, and traceable workflows. The sandbox supports governed onboarding, project-based collaboration, and reusable evaluation evidence through persistent records and audit logging.
Collaborative AI experimentation in industry-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-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.