Building a Production-Ready AI Security Foundation

Scaling Generative AI applications from proof-of-concept to production is often bottlenecked by security concerns, specifically sensitive data exposure and prompt injection.

Establishing a production-ready posture requires a defense-in-depth strategy across three layers:

• Application Layer: Real-time threat detection and mitigation.

• Data Layer: Enforcing privacy controls and compliance.

• Infrastructure: Network segmentation and compute isolation.

To implement these controls, this guide details three hands-on labs focused on securing these specific architectural planes.

Protect the Application in Real-Time: Model Armor

The application layer, where users directly interact with your AI model, is the most exposed surface in a GenAI application. This surface is frequently targeted by attackers using prompts and responses to exploit vulnerabilities.

This lab focuses on securing the application and model layers by demonstrating how to deploy a comprehensive security service called Model Armor. Model Armor acts as an intelligent firewall, analyzing prompts and responses in real-time to detect and block threats ...