Unlocking the security challenges in Retrieval-Augmented Generation (RAG) systems is critical for business leaders steering AI innovation. This episode unpacks how advanced AI models can increase security risks, why layered defenses are essential, and what practical steps you can take to protect your enterprise data.

In this episode:

- Why smarter AI models like GPT-4o can be more vulnerable to prompt probe attacks

- The unique security risks posed by RAG’s blend of AI and sensitive data

- Real-world legal and financial consequences from AI-generated errors

- Defense strategies including human review, secondary AI checks, and automated red teaming

- How Guardian LLMs act as gatekeepers to block malicious queries

- Tactical tools and frameworks to implement layered RAG security

Key tools and technologies mentioned:

- OpenAI GPT-4o and GPT-3.5

- LangChain framework with RunnableParallel

- python-dotenv for secrets management

- Giskard’s LLM scan for automated red teaming

- Git for version control

Timestamps:

0:00 - Introduction to Security in RAG

3:15 - Why Smarter AI Means New Risks

6:30 - Real-World Security Failures and Legal Cases

9:45 - Defense Approaches: Red Teaming and Guardian LLMs

13:10 - Under the Hood: How Guardian LLMs Work

16:00 - Balancing Latency, Cost, and Security

18:30 - Tactical Tools and Best Practices

20:00 - Closing Thoughts and Resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Memriq AI: https://memriq.ai