Adversarial AI: Attacking, Defending & Governing ML Systems

As AI becomes central to cybersecurity defence, attackers are increasingly targeting the AI systems themselves. Model poisoning, adversarial inputs, backdoor exploits, and model stealing are active threats — and most security teams are unprepared to detect or defend against them. This course gives you the knowledge and practical strategies to secure ML systems from the inside out. You'll examine how ML systems are manipulated through adversarial inputs, poisoning attacks, and threat models across real-world use cases including malware detection and fraud analytics. You'll then explore advanced attack vectors: model poisoning, information leakage, model stealing, and backdoor exploits, and assess their impact on data privacy, intellectual property, and user safety. From attack to defence, you'll learn to apply secure algorithm design, differential privacy, and guardrail protection — and conduct AI security testing using red, purple, and blue teaming approaches. The course closes with AI governance: responsible AI principles, bias mitigation, transparency, data ethics, and the global regulatory frameworks governing AI in cybersecurity. Designed for security analysts, ML engineers, security architects, and risk and compliance professionals working with AI-powered security systems. Job skills taught: Adversarial AI Defence · AI Security Testing · ML Threat Modelling · Model Robustness · Differential Privacy · Red/Blue/Purple Teaming · AI Governance · Responsible AI · Regulatory Compliance for AI Features Coursera Coach, Dialogues and Role Plays - 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.