
Understand data privacy and confidentiality risks in public generative AI, including data leakage, third-party processing, and regulatory exposure under GDPR, HIPAA, and PCI DSS, with opt-out and anonymization considerations.
Demonstrates prompt injections in a hands-on sandbox, escalating levels to reveal how attackers coax a password from a mini LLM using storytelling, translation, and encoding tricks for security teams.
Explore how denial-of-service attacks threaten the availability of generative AI systems, with risk management strategies such as local hosting, threat modeling, due diligence on third-party providers, and security testing.
Explore model bias in AI, illustrated by a Bloomberg study on Stable Diffusion that shows skews in skin tone and gender across occupations, highlighting training data diversity and transparency needs.
Understand copyright risks in generative AI, where tools like ChatGPT, Midjourney, and Stable Diffusion train on copyrighted data without consent, creating legal exposure and requiring policy controls.
Generative AI is transforming how the world works - from coding and design to decision-making and automation. Tools like ChatGPT, Claude, and Midjourney are revolutionizing industries, but they also introduce new security and governance risks that most professionals aren’t prepared for.
The “Generative AI – Risk and Cybersecurity Masterclass 2026” gives you a complete understanding of how these systems work — and how to secure them. You’ll learn the core principles, components, and threat surfaces of generative AI systems, along with practical strategies, frameworks, and controls to manage emerging AI risks effectively.
What You Will Learn
Fundamental principles and components of Generative AI
Understanding the risk landscape in Generative AI and its implications
Strategies for identifying, mitigating, and managing risks in Generative AI
Unique Risks like Prompt Injections, Hallucinations, Data Poisoning etc.
Techniques and guidelines for implementing a robust security architecture within Generative AI systems
Course Outline
Introduction to Generative AI
What is Generative AI?
Why is understanding risks and security in Generative AI important?
Risks in Generative AI
Overview of the Generative AI risk landscape
Detailed analysis of potential risks and their implications
How these risks can have a real life impact
Security in Generative AI
Implementing a security framework for Generative AI systems
Key challenges to overcome
Techniques to assess and improve the security posture of a Generative AI system
Who Should Take This Course
This course is designed for individuals interested in understanding and managing the risks associated with Generative AI, including:
AI practitioners
Cybersecurity professionals
Data Scientists
IT Managers
Anyone interested in learning about Generative AI and its risks
Prerequisites
This course assumes a basic understanding of AI and cybersecurity, but no prior knowledge of Generative AI is required.
Instructor
A multi-award winning, information security leader with over 20+ years of international experience in cyber-security and IT risk management in the fin-tech industry. Winner of major industry awards such as CISO of the year, CISO top 30, CISO top 50 and Most Outstanding Security team.
Taimur's courses on Cybersecurity and AI have thousands of students from all over the world. He has also been published in leading publications like ISACA journal, CIO Magazine Middle East and published two books on AI Security and Cloud Computing ( ranked #1 new release on Amazon )