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Security in RAG (Chapter 5)
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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
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