Harshavardhan Peddireddy of Meijer, Inc.

Enterprise AI systems depend on large volumes of rich data, and much of that data is personal and regulated. This session looks at how organizations are resolving that tension with synthetic data, which is artificially generated data that preserves the statistical patterns of real data without copying any real record. Participants will see what synthetic data actually is, how it is generated, and where enterprises are using it today, including software testing, AI model training, analytics, and safe data sharing. The session also explains why traditional masking and anonymization fall short, and then follows a single piece of data through a modern AI pipeline to show where privacy quietly breaks, including training data memorization and the collapse of consent boundaries in foundation models. It closes with the practical controls and governance steps that keep these systems trustworthy. Attendees will leave with a clear understanding of how synthetic data works, where it fits in an enterprise AI strategy, and what questions to ask of any AI system that touches personal data.

Biography:

Harshavardhan Peddireddy is a Platform Architect at Meijer Inc. and a PhD Candidate in Artificial Intelligence at the University of the Cumberlands. He has more than 18 years of experience in data privacy engineering across the healthcare insurance, enterprise financial services, and retail industries, with hands-on work in data masking, synthetic data generation, tokenization, and de-identification for environments regulated under HIPAA and PCI DSS. He is an IEEE Senior Member and a Fellow of Information Privacy with the International Association of Privacy Professionals, and holds the CIPP/US, CDPSE, AIGP, and PMP certifications. He serves as a peer reviewer for IEEE Access, Springer Nature, and Wiley, and is a regular invited speaker on synthetic data, privacy engineering, and AI governance at international conferences and professional forums.

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