Description:

Who it’s for: This advanced course on Gen AI Security is designed for learners who have a foundational understanding of Artificial Intelligence and are interested in the security aspects of generative models. Ideal for students, professionals, cybersecurity enthusiasts, and AI practitioners who want to delve deeper into the potential security risks and mitigation strategies associated with Generative AI.

What it is: This course provides a thorough examination of the security landscape of Generative AI. It explores the unique risks posed by generative models, the methods to mitigate these risks, and real-world applications and case studies that illustrate these concepts in action.

What you’ll learn:

Introduction to Generative AI:

Gain a solid understanding of Generative AI. Learn about its fundamental principles, key technologies, and how it differs from other types of AI models.

Security Landscape of Gen AI:

Explore the security challenges and vulnerabilities unique to Generative AI. Understand the potential threats, such as data poisoning, adversarial attacks, and misuse of AI-generated content.

Mitigating Gen AI Security Risks:

Learn about strategies and best practices to mitigate security risks in Generative AI. This includes robust model training, validation techniques, and ethical considerations to ensure AI systems are secure and reliable.

Case Studies and Applications:

Through detailed case studies, discover how Generative AI security risks manifest in real-world scenarios. Learn from practical examples of both security breaches and successful mitigation efforts, applying these lessons to your own work.

By the end of this course, you’ll have a comprehensive understanding of the security issues surrounding Generative AI and how to address them. You’ll be equipped with the knowledge and skills to protect AI systems from potential threats, ensuring their safe and ethical deployment in various applications.