In this episode of Memriq Inference Digest - Engineering Edition, we explore the critical security challenges in Retrieval-Augmented Generation (RAG) systems, unpacking insights from Chapter 5 of Keith Bourne’s 'Unlocking Data with Generative AI and RAG.' Join us as we break down real-world vulnerabilities, defense strategies, and practical implementation patterns to build secure, production-ready RAG pipelines.

In this episode:

- Understand why advanced LLMs like GPT-4o can be more vulnerable to prompt probe attacks than earlier models

- Explore layered security architectures including relevance scoring and multi-LLM defenses with LangChain

- Learn how secrets management and automated adversarial testing strengthen your RAG system

- Compare manual and automated red teaming approaches and their trade-offs in production

- Hear real-world cases highlighting the legal and financial stakes of hallucinations and data leaks

- Get practical tips for building and maintaining defense-in-depth in enterprise RAG deployments

Key tools & technologies mentioned:

- OpenAI GPT-4o and GPT-3.5

- LangChain (RunnableParallel, StrOutputParser)

- python-dotenv for secrets management

- Giskard’s LLM scan for adversarial testing

- Git for version control and traceability

Timestamps:

00:00 - Introduction and episode overview

02:30 - The surprising vulnerabilities in advanced LLMs

05:15 - Why security in RAG matters now: regulatory and technical context

07:45 - Core security concepts: retrieval as both risk and opportunity

10:30 - Comparing red teaming strategies: manual vs automated

13:00 - Under the hood: Guardian LLM architecture with LangChain

16:00 - Real-world impact: hallucinations, legal cases, and mitigation

18:30 - Practical toolbox: secrets management, relevance scoring, and continuous testing

20:00 - Closing thoughts and book spotlight

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