Sold by: SANS Institute
Deployed on AWS
Learn to build secure, self-hosted LLM solutions using Retrieval Augmented Generation. Progress from traditional RAG to Contextual RAG and Agentic RAG while implementing access controls and defending against prompt injection - all without exposing sensitive data to third-party APIs.
Overview
Build secure, self-hosted RAG systems that leverage LLMs without exposing sensitive data to third parties. This fully hands-on course teaches everything needed to implement LLM-based solutions for question answering, knowledge retrieval, and policy creation.
Work alongside the instructor to build a complete Retrieval Augmented Generation system:
- Host LLMs locally and efficiently
- Interface with LLMs programmatically
- Preprocess and ingest documents into vector databases
- Engineer effective RAG prompts
- Implement access controls limiting data disclosure per user
- Defend against prompt injection attacks
Progress from traditional RAG to advanced Contextual RAG, improving response quality while maintaining data security. Implement Agentic RAG with auditor and prompting agents for sophisticated use cases.
Everything runs on local containers - no sensitive data leaves your environment. The techniques translate directly to commercial APIs when needed, but this course proves you can build powerful LLM solutions entirely in-house.
Requires intermediate Python skills. 7 CPEs.
Highlights
- Build end-to-end RAG systems, implement Contextual RAG for improved responses, and deploy Agentic RAG with auditor and prompting agents - all self-hosted
- Hands-on workshop format building a complete RAG solution with vector databases, access controls, and prompt injection defenses
- OnDemand course with 7 CPEs - ideal for professionals implementing enterprise AI solutions without third-party API dependencies
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Pricing
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Dimension | Description | Cost/12 months |
|---|---|---|
SEC495 - Single User | Single user license for AI - SEC495: Leveraging LLMs: Building & Securing RAG, Contextual RAG | $459.00 |
Vendor refund policy
Refunds available within 30 days if course not accessed.
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