Testing and Securing AI-Powered Applications

As artificial intelligence becomes a core component of modern software, ensuring the security and reliability of AI-integrated code is more critical than ever. Developers must navigate unique vulnerabilities, from insecure dependencies to prompt injection risks, to build robust systems. This text-based course guides you from foundational security concepts to practical testing strategies for AI-powered applications. You will learn how to analyze your code for vulnerabilities, evaluate third-party integrations, and implement robust testing workflows to ensure your AI systems behave predictably and securely. What you'll learn: - Understand foundational AI security concepts, terminology, and common vulnerability frameworks. - Audit and secure third-party dependencies and libraries used in your AI integrations. - Identify and mitigate common risks such as prompt injection and data leakage. - Implement automated testing strategies to evaluate the reliability and safety of AI outputs. - Apply secure coding practices for API integrations and data handling within AI pipelines. - Configure robust error handling to prevent information disclosure. The course begins with essential definitions and foundational security principles before moving into practical code auditing and testing methodologies. Through clear written explanations and structured code examples, you will build a solid framework for developing secure AI software. This course is designed for beginner developers, software engineers, and security enthusiasts who want to understand the fundamentals of AI code security with no prior security experience required. Start building safer, more resilient AI applications today.