Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911483552595968 |
|---|---|
| author | Waseem, Muhammad Islam, Md Aidul Shuvo, Md Nasir Uddin Hasan, Md Mahade Kemell, Kai-Kristian Rasku, Jussi Saari, Mika Saari, Vilma Pajasmaa, Roope Oivo, Markku Abrahamsson, Pekka |
| author_facet | Waseem, Muhammad Islam, Md Aidul Shuvo, Md Nasir Uddin Hasan, Md Mahade Kemell, Kai-Kristian Rasku, Jussi Saari, Mika Saari, Vilma Pajasmaa, Roope Oivo, Markku Abrahamsson, Pekka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03394 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned Waseem, Muhammad Islam, Md Aidul Shuvo, Md Nasir Uddin Hasan, Md Mahade Kemell, Kai-Kristian Rasku, Jussi Saari, Mika Saari, Vilma Pajasmaa, Roope Oivo, Markku Abrahamsson, Pekka Software Engineering 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. |
| title | Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2603.03394 |