Protecting Sensitive Data in Retrieval-Augmented Generation (RAG) - Video
As enterprises embrace generative AI to accelerate decision-making and improve customer experiences, one challenge becomes clear: large language models alone lack the internal context needed to produce accurate, business-aligned insights.
Retrieval-Augmented Generation (RAG) bridges that gap by injecting trusted organizational knowledge into AI responses, enabling systems that are informed, precise, and grounded in your unique data. But because RAG touches sensitive information at every stage—from ingestion and embedding to retrieval and generation—keeping that data secure is essential.
This video explores how the RAG pipeline works and outlines the critical security considerations organizations must address to deploy it safely. You’ll learn where sensitive data is most exposed, how to enforce protection policies consistently across hybrid environments, and what controls are needed to maintain compliance while enabling rapid AI innovation. Whether you’re building customer-facing AI tools or enhancing internal knowledge systems, this guide will help you adopt RAG with confidence and ensure trust is built into every interaction.
What You'll Learn:
- How RAG enhances enterprise AI with accurate, context-aware intelligence
- Where sensitive data is exposed across the RAG pipeline—including ingestion, chunking, embeddings, vector storage, retrieval, and generation
- Best practices for protecting vector databases and securing embeddings without changing your applications
- How to enforce consistent data security controls such as tokenization, masking, and encryption across cloud and hybrid environments
- Why maintaining independent ownership of encryption keys is essential for secure cloud-based AI deployments
- How real-time monitoring, anomaly detection, and audit trails strengthen AI governance and compliance
- Steps to build a secure, scalable, and trustworthy RAG architecture that accelerates enterprise AI adoption