Every LLM application has two doors to guard: what users send in (PII, prompt attacks, abusive content) and what the model sends back. A single filter is a single point of failure — one regex gap, one language it doesn't cover, one clever jailbreak, and your safeguard is gone.
In this hands-on, we'll build a two-layer defense: Amazon Comprehend screens every input for PII before it gets anywhere near a model, and Amazon Bedrock Guardrails enforces content and PII policy at the model boundary itself. Two independent layers, two different detection engines, so the second catches what the first misses.
Prefer video? This entire hands-on is also on YouTube:
What we'll build
user input
