On the Challenges of Deploying Privacy-Preserving Synthetic Data in the
  Enterprise
Lauren Arthur Jason Costello Jonathan Hardy Will O'Brien James Rea Gareth Rees Georgi Ganev
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.