AI Sandbox: Technical Report

2026-08-02

AI Summary

This work addresses the challenge that existing AI experimentation platforms struggle to simultaneously support rapid prototyping and governance requirements such as access control, tenant isolation, and process transparency. The authors propose and implement a governance-aware, multi-tenant AI sandbox platform featuring a layered architecture that decouples the user interface, control plane, and execution layer. The platform integrates approval workflows, audit logging, and configuration persistence mechanisms, and innovatively combines structured experimentation with cross-project reusable evaluation evidence generation, thereby establishing persistent linkages between governance decisions and experimental data. Deployed in an industry–academia collaboration setting, the platform demonstrates its effectiveness in enabling controlled collaboration, traceable experiments, and cross-project result comparison, offering a reusable reference architecture and practical insights for integrating governance into AI development environments.

Abstract

Collaborative AI experimentation across industry and academia requires platforms that enable rapid prototyping while preserving controlled access, tenant separation, and transparent workflows. Despite growing interest in AI sandboxes, there is still limited practical guidance on how to design and implement platforms that integrate experimentation capabilities with governance requirements. This work presents the design and implementation of a governance-aware, multi-tenant AI sandbox for structured experimentation and the generation of reusable evaluation evidence across projects and stakeholder groups. The sandbox was developed within an industry-academia collaboration based on requirements that were iteratively refined with industrial partners. Its reference architecture separates the multi-tenant user interface from the backend control plane and places execution and data-management functions in dedicated layers. The platform supports governed user onboarding, project-centered collaboration, managed access to AI services, approval workflows, audit logging, and traceable experimentation. Experiment configurations, contextual information, and governance decisions are stored as persistent records, allowing evidence and outcomes to be compared and reused across projects. The development process provides practical lessons for deploying and extending governance-aware AI sandbox platforms in collaborative research and industrial environments.

Problem

• AI sandbox
• multi-tenant
• governance
• collaborative experimentation
• controlled access

Innovation

• governance-aware AI
• multi-tenant sandbox
• traceable experimentation
• collaborative AI platform
• evidence reuse