Overview
Deploying LLMs in production requires robust security: preventing prompt injection, protecting sensitive data, enforcing access control, and meeting compliance requirements. This module covers the full AI security stack — from input guardrails to output filtering, PII masking, LLM gateways with RBAC, and multi-tenant data isolation patterns.
Concept Flashcards
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System Architecture
1 interactive diagram — drag nodes · scroll to zoom · click for details
A layered defense for LLM APIs: input guardrails → authentication → RBAC → rate limiting → PII masking → LLM call → output guardrails → audit log.
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Auth / RBAC
Guardrails / Block
PII Masking
Audit
Key Concepts
6 concepts — click to expand
Prompt injection occurs when user-controlled input manipulates the LLM's behavior: direct injection (overriding system prompt) or indirect injection (malicious content in retrieved documents). Defense: input validation, canary tokens, role-based prompt separators, and output validation that checks for policy violations.
Tech Stack
NeMo GuardrailsLlamaFirewallLLM GuardGuardrails AIBedrock GuardrailsPresidio
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