Generative Ai Security: Protecting Data & Models Masterclass

Learn GenAI Security, AI Threat Modeling, Data Protection, and Model Safeguarding with Real-World Examples & Techniques.

What you'll learn

Understand the fundamentals of Generative AI and why security plays a critical role in its responsible use.

Identify common cybersecurity threats specific to GenAI tools and how they impact systems and users.

Analyze real-world GenAI cyber incidents to uncover security lapses and best practices.

Learn the basics of AI threat modeling and how to build secure-by-design AI systems.

Explore data security challenges in GenAI and how to apply protection techniques effectively.

Discover techniques like DRM, watermarking, and model encryption to secure your AI models.

Requirements

No prior experience in cybersecurity or AI is required—this course is beginner-friendly.

A curious mindset and basic understanding of AI concepts will be helpful.

Access to the internet and a laptop or mobile device for viewing the lessons.

Description

As Generative AI (GenAI) continues to revolutionize industries, it also introduces a new frontier of cybersecurity threats. From model theft and prompt injection to data leaks and algorithmic manipulation—there are critical risks every AI professional, developer, and business leader must understand.This course is designed to help you understand the intersection of GenAI and cybersecurity in a practical, beginner-friendly way. You’ll explore how GenAI systems can be attacked, what threat modeling looks like for AI workflows, and how to safeguard sensitive data and intellectual property. Whether you're working on AI projects, auditing digital systems, or simply exploring the future of technology, this course will equip you with essential knowledge to make GenAI systems more secure and responsible. What you’ll learn:Core concepts of GenAI and why cybersecurity matters more than everCommon threats, risks, and real-world GenAI attack examplesAI threat modeling fundamentalsData security issues and how to protect data in GenAI workflowsHow to secure AI models from theft, misuse, and replicationTechniques like DRM, watermarking, and obfuscation for protectionNo prior AI or cybersecurity experience is required. If you’re a student, tech professional, founder, or just GenAI-curious—this course is for you.Join now and start securing the future of AI—one model at a time.

Overview

Section 1: Introduction

Lecture 1 What is Gen AI

Lecture 2 Why Security Matters in AI?

Lecture 3 Common Threats

Lecture 4 Real World Examples

Lecture 5 Key Takeaways on GenAI and Cybersecurity

Section 2: AI Security Standards, Compliance, and Ethics

Lecture 6 Security Frameworks for AI

Lecture 7 Regulatory and Legal Considerations

Lecture 8 Ethical and Responsible AI

Lecture 9 Case Study Analysis

Section 3: AI Threat Modelling for Generative AI

Lecture 10 Understanding AI Threat Modelling Fundamentals

Lecture 11 AI-Specific Attack Vectors

Lecture 12 Threat Modeling Frameworks

Lecture 13 Practical Threat Modeling Exercise

Section 4: Data Security

Lecture 14 Understanding Data Security in GenAI

Lecture 15 Key Threats to AI Data Security

Lecture 16 Data Protection Techniques for GenAI

Lecture 17 Key Takeaways Data Security in Generative AI

Section 5: Protecting AI Models

Lecture 18 Protecting AI Models from theft

Lecture 19 Secure your AI Models

Lecture 20 Protecting AI Models with DRM and Watermarking

Lecture 21 Real world examples

Section 6: Securing Model Deployment and APIs

Lecture 22 Secure Infrastructure for AI

Lecture 23 API Security

AI Enthusiasts and Beginners who want to understand how to build and use GenAI tools securely.,Cybersecurity Learners looking to explore emerging threats and protection methods in the world of AI.,Tech Professionals and Developers working with AI models who want to prevent data leaks and model theft.,Students of Data Science or Computer Science who want to gain practical awareness of AI-related risks.,Startup Founders and Product Managers integrating GenAI into their business workflows and needing to secure it.,IT Auditors and Compliance Officers who must understand the risk posture of AI systems and how to safeguard them.,Educators and Trainers designing AI-focused courses who want to add a layer of security awareness to their content.