LLM guardrails require a four-layer defense architecture covering input validation, output filtering, behavioral/dialog policy, and retrieval/tool controls. The post maps this architecture against the OWASP Top 10 for LLM Applications 2025, EU AI Act timelines, and NIST AI 600-1. It compares open-source options (Guardrails AI, NeMo Guardrails, Llama Guard 4, Presidio) with cloud-native offerings (Amazon Bedrock Guardrails, Azure Prompt Shields, Google Model Armor) and specialist vendors (Lakera Guard, Cisco AI Defense, Protect AI). A reference architecture places enforcement at an AI gateway with fail-closed defaults for customer-facing surfaces. The post also covers evaluation methodology, automated red-teaming tools (Garak, PyRIT, promptfoo), key metrics (block recall, false-refusal rate, latency overhead), cost optimization strategies, a 90-day rollout cadence, and common anti-patterns to avoid.
