Scaling Generative AI applications from proof of concept to production environments is often bottlenecked by security concerns, particularly sensitive data leakage and prompt injection.
To prepare your production environment, you need to: Defense-in-depth strategy Across three layers:
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Application layer: Real-time threat detection and mitigation.
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Data layer: Enhanced privacy controls and compliance.
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Infrastructure: Network segmentation and compute separation.
To implement these controls, this guide details three hands-on labs that focus on the security of these specific architectural aspects.
Protect your applications in real time: Model Armor
The application layer, where the user interacts directly with the AI model, is most exposed surface Within the GenAI application. This surface is often targeted by attackers who use prompts and responses to exploit vulnerabilities.
This lab focuses on application layer and model layer security 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 before they can cause harm.
In this lab, you will learn how to mitigate critical risks such as:
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Prompt injection and jailbreak: Malicious users create prompts to bypass safety guardrails or extract sensitive data. Create a Model Armor security policy to automatically detect and block these attempts.
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Malicious URL detection: Block users who embed dangerous links in prompts that could be part of an indirect injection.
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Sensitive data leaked: Prevents models from accidentally exposing personally identifiable information (PII) in their responses.
Main components:
Create reusable templates that define what Model Armor analyzes, detects, and blocks. of block-unsafe-prompts The template targets malicious input, but data-loss-prevention Templates prevent sensitive data from being exposed in prompts or responses.
After completing this lab, you will have a blueprint for integrating Model Armor directly into your application’s backend API so that all requests to your model first pass through this real-time threat detection layer.
