Module 12: Security for RAG Systems Slides

Slide Outline

1. Security for RAG Systems - Prompt injection defense, data leakage prevention, vector DB security, and AI access control
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - A single prompt injection or data leakage incident destroys user trust. AI security is not optional for production syste
4. Prompt Injection - Lesson section from the full module
5. Data Leakage Prevention - Lesson section from the full module
6. Vector Database Security - Lesson section from the full module
7. Common Mistakes to Avoid - 4 mistakes covered
8. Hands-On Labs - 2 hands-on labs
9. Key Takeaways - 5 points to remember

Learning Objectives

• Defend against prompt injection attacks
• Prevent data leakage across tenants
• Secure vector database access with authentication
• Implement AI-specific access control policies

Why This Module Matters

A single prompt injection or data leakage incident destroys user trust. AI security is not optional for production systems — it is a requirement. This module teaches the AI-specific threats that traditional security training misses.

Common Mistakes

• Not validating user input before passing to the LLM
• Optional tenant filtering (developers forget, attackers exploit)
• No output filtering (sensitive data in retrieved docs leaks to users)
• Trusting all ingested documents (malicious docs can poison retrieval)

Key Takeaways

• Prompt injection is the #1 AI security threat — validate inputs, filter outputs
• Multi-tenant data isolation must be mandatory, not optional
• Vector databases need the same security as any database (auth, encryption, audit)
• Context poisoning (malicious documents) requires document validation at ingestion
• RAG security = application security + AI-specific defenses

Hands-On Labs

1. Prompt Injection Defense

   Test and defend against prompt injection attacks.

   35 min - Advanced

   • Test your RAG system with prompt injection attacks
   • Implement input validation and sanitization
   • Add output filtering for sensitive data patterns
   • Verify defenses against 10 attack variations

   View lab files on GitHub

2. Secure Multi-Tenant Vector APIs

   Prevent data leakage between tenants.

   30 min - Advanced

   • Add mandatory tenant_id filtering to all queries
   • Test cross-tenant isolation
   • Add authentication to vector DB API
   • Implement audit logging for all retrievals

   View lab files on GitHub