As artificial intelligence and machine learning rapidly evolve, so do the security threats and ethical challenges surrounding them. In this essential course, AI Security and Responsible AI Practices, you'll gain the critical knowledge needed to build, deploy, and manage AI systems that are not only effective—but also secure, ethical, and privacy-conscious.
Taught by renowned experts Omar Santos and Dr. Petar Radanliev, this course equips professionals with a practical and forward-looking understanding of AI security. You'll explore how to safeguard AI and ML systems against cyberattacks, ensure data integrity, and uphold privacy through real-world examples from widely used tools like ChatGPT, GitHub Copilot, DALL·E, Midjourney, and Stable Diffusion.
What You’ll Learn:
Core principles of AI/ML system security, including threat modeling and attack vectors
How to detect, prevent, and respond to adversarial AI threats
Data protection strategies: anonymization, encryption, and regulatory compliance
Responsible AI development practices that address fairness, transparency, and accountability
Security applications of generative AI and large language models (LLMs)
Emerging trends in AI ethics, privacy, and future challenges in governance
Who Should Enroll:
Ideal for AI/ML engineers, cybersecurity professionals, risk managers, and IT leaders who are building or overseeing AI-driven systems. This course is also valuable for developers using generative AI tools who want to integrate responsible and secure practices into their workflows.
Why Take This Course:
With growing reliance on AI in critical systems, there's no room for oversight in ethics or security. Gain the skills and awareness to protect users, data, and systems—while aligning with global standards for trustworthy AI.