AI Security with Model Armor: Protecting Generative AI Deployments

As generative AI becomes central to modern software, securing these systems against emerging vulnerabilities is more critical than ever. Organizations must protect their large language models from malicious inputs, data leaks, and inappropriate outputs without compromising performance. This written course provides a solid foundation in AI security, focusing on how to safeguard your deployments using Model Armor. You will start by learning key AI safety terminology and core concepts before moving on to practical configuration strategies. Through clear, written explanations and code snippets, you will discover how to establish robust filters that protect both your users and your models. What you'll learn: - Understand the core principles of AI safety, prompt injection, and model vulnerabilities - Configure Model Armor to filter toxic, harmful, or inappropriate content in real time - Implement PII masking and data loss prevention strategies for inputs and outputs - Detect and mitigate jailbreak attempts and adversarial prompt engineering - Integrate Model Armor policies into modern application development workflows - Analyze security logs to continuously refine safety thresholds and policies The course begins with foundational concepts in AI security, ensuring you understand the threat landscape before exploring practical setup and policy design. You will then study real-world scenarios to learn how to test and adapt your security configurations for production environments. This course is designed for developers, security beginners, and IT professionals looking to secure their AI integrations. No prior experience with AI security tools is required. Start reading today to build secure, reliable, and trustworthy AI applications.