Generative AI technologies are gaining unprecedented popularity, causing a mix of excitement and apprehension through their remarkable capabilities. In this paper, we study the challenges associated with deploying synthetic data, a subfield of Generative AI. Our focus centers on enterprise deployment, with an emphasis on privacy concerns caused by the vast amount of personal and highly sensitive data. We identify 40+ challenges and systematize them into five main groups -- i) generation, ii) infrastructure & architecture, iii) governance, iv) compliance & regulation, and v) adoption. Additionally, we discuss a strategic and systematic approach that enterprises can employ to effectively address the challenges and achieve their goals by establishing trust in the implemented solutions.

翻译：生成式人工智能技术正以前所未有的速度普及，其卓越能力既引发兴奋也带来隐忧。本文聚焦于生成式人工智能的子领域——合成数据的部署挑战。我们重点关注企业级部署场景，尤其关注海量个人及高度敏感数据引发的隐私问题。研究共识别出40余项挑战，并将其系统归纳为五大类：i）数据生成，ii）基础设施与架构，iii）治理机制，iv）合规与监管，以及v）应用采纳。此外，我们探讨了企业可采用的战略性与系统性方法，通过建立对解决方案的信任机制，有效应对挑战并实现既定目标。