AI Security Fundamentals – LLM Threats & OWASP 2026

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you’ll gain a comprehensive understanding of the security principles vital for securing Large Language Model (LLM) applications. You will explore critical vulnerabilities, such as prompt injection, data poisoning, and improper output handling, while learning strategies to mitigate these risks. Through engaging modules, you will analyze real-world examples of successful and unsuccessful LLM implementations, enabling you to understand the delicate balance between functionality and security in AI systems. The course is structured across 12 detailed modules, beginning with an introduction to LLM applications and their associated security challenges. As you progress, you will dive into specific topics such as prompt injection, sensitive information disclosure, and supply chain vulnerabilities, with each module providing practical, hands-on solutions to counter these risks. You’ll also explore essential topics such as the role of third-party models, data minimization, and model poisoning, which are key to securing AI applications at scale. Designed for security professionals and AI developers, this course provides you with the tools needed to address security issues within LLM systems proactively. You’ll walk away with the ability to implement best practices for securing LLM development and deployment processes. Whether you are working in AI development, security, or policy, this course will help you understand and address the security complexities that come with LLM technology. By the end of the course, you will be able to assess LLM vulnerabilities, apply security principles to mitigate risks, design secure LLM applications, and implement strategies to defend against prompt injections and other security threats.

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