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

Keywords: synthetic data, deployment, enterprise, challenges, privacy

TL;DR: We identify 40+ challenges of deploying privacy-preserving synthetic data and systematize them into five main groups –- i) generation, ii) infrastructure & architecture, iii) governance, iv) compliance & regulation, and v) adoption.

Abstract:

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.

Submission Number: 28