How synthetic data enables PrivacySafe AI at enterprise dcale

By Mayank Baid, Regional Vice President, India & South Asia, Cloudera

As AI becomes embedded deeper into everyday operations, enterprises are feeding more data into models than ever before. Large language models (LLMs) are now common in customer support, analytics, developer productivity, and knowledge management. AI agents add another layer: systems that can retrieve information, reason over it, and take action across tools and workflows.

However, this presents an uncomfortable reality for consumers: the most valuable data for improving AI performance is often the most sensitive. Support transcripts, case notes, transaction histories, and operational logs can contain personally identifiable information (PII), regulated attributes, or proprietary business context. For instance, India’s Digital Personal Data Protection (DPDP) Act, data carries clear obligations around consent, purpose limitation, and accountability. However, even with strong intentions to uphold privacy, it is easy for sensitive fields to slip into training corpora, evaluation ...