Generative AI is transforming how organizations create content, automate processes, support decision-making, and deliver digital services. However, these systems also introduce new cybersecurity risks, including prompt injection, data leakage, adversarial attacks, model manipulation, insecure integrations, and unauthorized access. This course provides a structured and practical introduction to protecting Generative AI systems throughout their entire lifecycle.
You will begin by exploring the core concepts of GenAI cybersecurity and examining the most important threat vectors affecting AI models, applications, data pipelines, APIs, and supporting infrastructure. The course explains adversarial machine learning techniques and demonstrates how attackers may attempt to influence model behavior, compromise training data, extract sensitive information, or exploit weaknesses in AI-enabled applications.
You will then examine AI firewalls and learn how they can monitor, filter, and control interactions with Generative AI systems. The course covers the purpose, architecture, and operation of AI firewalls, followed by practical implementation considerations and real-world case studies.
A dedicated section introduces AI Security Posture Management. You will learn how AI-SPM solutions help organizations identify vulnerabilities, monitor AI assets, evaluate configurations, manage risks, and maintain visibility across complex GenAI environments. You will also explore the technologies that support modern AI security programs.
The course examines advanced defensive strategies, including Zero Trust architecture, behavioral analytics, and AI-powered incident response. You will learn how to apply identity verification, least-privilege access, continuous monitoring, anomaly detection, and automated response techniques to protect GenAI models, users, data, and infrastructure.
Secure development is another major focus. You will learn how to design safer GenAI applications, apply secure coding practices, protect data pipelines, and integrate cybersecurity controls throughout the AI development lifecycle. Ethical considerations such as privacy, fairness, transparency, responsible use, and accountability are also addressed.
The final sections cover the regulatory and compliance landscape surrounding Generative AI. You will explore AI security regulations, compliance requirements, audit practices, and methods for demonstrating that appropriate controls are operating effectively.
The course concludes with emerging trends, including AI-versus-AI cybersecurity, the possible impact of quantum computing, and future innovations in GenAI protection. By completing the course, you will gain a comprehensive understanding of how to identify, manage, and reduce security risks across Generative AI systems.
