On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise

TL;DR

This paper examines the challenges enterprises face when deploying privacy-preserving synthetic data generated by AI, highlighting key issues across technical, governance, and regulatory domains, and proposing strategic solutions.

Contribution

It systematically categorizes over 40 challenges in deploying synthetic data in enterprises and offers a strategic approach to address them effectively.

Findings

Identification of 40+ challenges in synthetic data deployment

Systematic categorization into five main challenge groups

Proposed strategic approach for enterprise trust and compliance

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.