Enterprise RAG Security Vector DBs & Data PrivacyPublished 6/2026
Created by Dr. Amar Massoud
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 32 Lectures ( 3h 8m ) |
Size: 2.3 GB
Build secure RAG pipelines: vector-DB access control, PII protection, prompt-injection & leakage defense.What you'll learn⚡ Threat-model a RAG pipeline using the OWASP Top 10 for LLM Applications (2025)
⚡ Rebuild authorization at the vector layer with query-time RBAC and ABAC metadata filtering
⚡ Secure ingestion with data classification, PII/PHI masking, redaction, and pseudonymization
⚡ Defend against direct and indirect prompt injection with input and output guardrails
⚡ Prevent system-prompt leakage and sensitive-information disclosure in responses
⚡ Harden vector databases with authentication, encryption, network isolation, and multi-tenancy
❗ Comfort reading Python and running scripts locally (the hands-on labs use Python)
❗ Basic familiarity with LLMs and the idea of RAG is helpful but not required
❗ A general security or software-engineering background helps but is not mandatory
DescriptionThis course contains the use of artificial intelligence.
Retrieval-Augmented Generation (RAG) is how enterprises put their own data behind an LLM, and it is quietly one of the riskiest things you can build. The moment you chunk and embed your documents, their original access permissions are stripped and left behind. Unless you rebuild authorization at the vector layer, any user who can query the assistant can potentially retrieve any ingested chunk: another department's records, a customer's PII, a competitor's contract, or your own system prompt.
This course teaches you to build secure RAG pipelines end to end. It is an example-driven program built around one running model company, Meridian Health and its internal assistant Atlas, followed across every lecture by staff security engineer Priya Nair. You will see exactly where data leaks, why embeddings are not anonymization, and how to layer controls across ingestion, the vector store, retrieval, generation, and operations. Everything is mapped to the OWASP Top 10 for LLM Applications (2025), with special focus on the RAG-native risks: vector and embedding weaknesses, prompt injection, sensitive information disclosure, and system prompt leakage.
You get four hands-on labs (secure ingestion, query-time access control, red-teaming, and right-to-be-forgotten), six build-toward assignments plus a portfolio capstone, and quizzes throughout. Map your controls to HIPAA, GDPR, SOC 2, and the EU AI Act. If you build, secure, or sign off on systems that put company data behind an LLM, this course gives you the mental model and the concrete controls to do it without leaking what you were trusted to protect.
Who this course is for⭐ Software, ML, and platform engineers building or operating RAG and LLM-powered applications
⭐ Security engineers and architects responsible for AI/LLM systems and data protection
⭐ Technical leads, AppSec, and GRC professionals who assess or sign off on enterprise RAG
⭐ Anyone putting proprietary or regulated data behind an LLM who needs to do it without leaking it
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