SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals

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Learn practical skills to build secure, effective Large Language Model (LLM) solutions using Retrieval Augmented Generation (RAG) in this fully hands-on course.
Great info around building proper chunking and good info for understanding RAG overall.
Learn to build secure, self-hosted LLM solutions using Retrieval Augmented Generation (RAG) for tasks like knowledge retrieval and policy creation, while implementing defenses against prompt injection and enforcing information sensitivity controls—all without relying on public APIs or third-party platforms.
David Hoelzer has fundamentally advanced cybersecurity by pioneering the GIAC Security Expert (GSE) certification, leading AI-driven threat detection initiatives, and developing MAVIS, an open-source ML tool enhancing code review processes.
Read more about David HoelzerExplore the course syllabus below to view the full range of topics covered in SEC495: Leveraging LLMs: Building & Securing RAG, Contextual RAG, and Agentic RAG.
Build secure, locally hosted LLM solutions from the ground up, progressing from traditional RAG to advanced Contextual and Agentic RAG while learning to enforce access controls, improve response quality, and protect sensitive data without relying on third-party APIs.
I have never dug into an LLM from this perspective, and I find I want to know more. This course will help me explain some of the ‘so what’ behind queries of LLMs.
“[The Retrieval Augmented Generation] lab was great — VERY applicable and important for real-world applications.
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