AI Security Fundamentals: Protecting LLM Applications with OWASP

As AI integration accelerates, understanding how to secure these powerful systems, especially large language models (LLMs), is crucial. This course provides the essential knowledge to protect AI applications from emerging threats. By the end of this course, you will be equipped to analyze AI systems for security weaknesses, apply robust defense mechanisms, and contribute to building more resilient and trustworthy AI solutions. What you'll learn: Understand the foundational principles of AI security and common attack vectors. Identify and classify prevalent threats to large language models (LLMs), including advanced prompt injection techniques. Apply the OWASP Top 10 security principles and specific OWASP for LLMs guidelines to AI application development. Implement secure design patterns and deployment strategies for AI systems. Analyze data governance, privacy implications, and ethical considerations within AI security frameworks. Practice evaluating AI systems for vulnerabilities through written exercises and case studies. The course begins with fundamental AI security definitions and progresses through common vulnerabilities, practical mitigation strategies, and best practices for securing LLM-powered applications. You will explore various attack surfaces and learn how to build defenses step-by-step. This course is designed for absolute beginners with no prior experience in AI security. It is ideal for developers, data scientists, security analysts, and anyone interested in understanding the security landscape of artificial intelligence. Start your journey toward becoming proficient in AI security and safeguarding the future of intelligent systems.