Regulatory Sandbox Design and Its Effectiveness in Governing Generative AI Deployment

Abstract

This study examines how regulatory sandbox design affects the governance effectiveness of generative AI deployment across controlled testing environments. The study develops a sandbox effectiveness framework based on five governance dimensions: entry governance, risk governance, monitoring governance, corrective governance, and exit governance. Using a composite Sandbox Effectiveness Index, the analysis shows that risk governance achieved the highest average score of 0.82, followed by monitoring governance at 0.79, entry governance at 0.68, corrective governance at 0.64, and exit governance at 0.61. These results indicate that sandbox programs were stronger in classifying and observing generative AI risks than in enforcing corrections and producing final deployment decisions. Risk mitigation analysis shows that compliance failure decreased from 74 to 29, privacy leakage from 71 to 34, misinformation exposure from 78 to 51, harmful automation from 66 to 45, and bias amplification from 69 to 48. Case-level results further show that higher monitoring intensity was associated with lower residual risk, where the most mature sandbox case recorded a monitoring score of 95, seventeen corrective interventions, and a residual risk score of 28. Contextual comparison shows that financial services achieved the highest overall effectiveness score of 0.79, followed by health information at 0.77, enterprise knowledge at 0.67, public service at 0.63, and education technology at 0.62. Final readiness classification identified two weak cases, seven moderate cases, eight strong cases, and three advanced cases. The findings confirm that regulatory sandboxes improve evidence quality, compliance discipline, and risk visibility, but they should not be treated as unconditional deployment approval. Effective generative AI governance requires stronger exit criteria, residual risk thresholds, enforceable correction, and post-deployment audit obligations.