GenAI is rewriting how enterprises use data - but it’s also rewriting your risk surface.
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LLMs thrive on rich, granular, structured and unstructured datasets that inevitably contain PII, PHI, payment data, IP, and other regulated information. Traditional controls like basic masking were never designed for this world, where we have models that learn from everything they see, remember what they shouldn’t, and can be prompted or attacked in unexpected ways.
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Masking/blocking sensitive data isn’t a GenAI strategy. It stalls innovation, creates fragmented data paths, and drives high-value teams toward shadow AI tools outside your control.As Generative AI reshapes how enterprises use data, it also introduces new privacy, security, and compliance challenges. Large Language Models (LLMs) rely on vast datasets often containing sensitive or regulated information, making data protection more complex than ever.

In this interactive workshop, Kalpesh Jajoo, Lead Solution Architect, and Aalap Mehta, Customer Experience Lead, will demystify how privacy vaults and data protection architectures can help organizations safely unlock GenAI’s full potential.

You’ll see live, practical scenarios including:

• Privacy-safe model training on sensitive and regulated datasets.
• Secure, compliant chatbots grounded in enterprise data.
• Controlled, privacy-preserving inference workflows that minimize data exposure.

All with one goal: to help you move from POC-and-pilot GenAI to production-grade, compliant GenAI with confidence.