Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned
Muhammad Waseem, Md Aidul Islam, Md Nasir Uddin Shuvo, Md Mahade Hasan, Kai-Kristian Kemell, Jussi Rasku, Mika Saari, Vilma Saari, Roope Pajasmaa, Markku Oivo, and Pekka Abrahamsson

TL;DR
This paper presents the design and implementation of a governance-aware AI sandbox supporting structured, traceable experimentation with reusable evaluation evidence, developed through iterative validation with industrial and academic partners.
Contribution
It introduces a layered architecture for a multi-tenant AI sandbox that integrates governance, access control, and traceability, with practical lessons learned from real-world deployment.
Findings
Supports controlled onboarding and collaboration
Enables reuse of evaluation evidence across projects
Provides practical guidance for deploying governance-aware AI platforms
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…
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Taxonomy
TopicsScientific Computing and Data Management · Ethics and Social Impacts of AI · Big Data and Business Intelligence
