Comprehensive Strategies for Data Protection

Sensitive customer data, proprietary algorithms and confidential business information are now routinely fed into generative AI systems that weren't architected with enterprise-grade security in mind. This fundamental disconnect has created a perfect storm for data leakage, with 73% of enterprises experiencing breaches averaging $4.8 million each in the past year alone. As these tools become embedded across critical workflows, the security risks are growing exponentially.

As enterprises rush to implement GenAI capabilities, security teams face unprecedented challenges in protecting confidential information across the entire AI pipeline—from prompt engineering to model training and output generation. Without the right safeguards in place, organizations risk exposing proprietary data, violating privacy regulations and undermining customer trust. Implementing comprehensive security controls specifically designed for AI workflows has become essential for responsible innovation.

Join Gopinath Manimayan, Software Solution Architect at UST to discover practical strategies for securing sensitive data throughout your GenAI ecosystem while maintaining AI functionality and performance.

Key Takeaways:

- Understand where and how sensitive data can leak in GenAI workflows (prompts, vector stores, outputs, etc.)
- Learn to implement effective guardrails for prompt input, model output and vector database layers
- Discover tools and frameworks for redaction, classification and AI content filtering
- Explore how confidential computing can protect AI inference in high-trust environments
- Gain actionable steps to align GenAI usage with privacy laws and enterprise compliance standards