Understanding AI Security Risks and Cybersecurity Fundamentals

As artificial intelligence becomes deeply integrated into modern software, it introduces entirely new security challenges that traditional cybersecurity frameworks are not equipped to handle. Understanding these unique vulnerabilities is essential for anyone looking to deploy or manage AI systems safely. This text-based course guides you through the foundational concepts of AI security, helping you recognize threats like prompt injection, data poisoning, and model evasion. You will learn how to evaluate AI trust, safety, and privacy risks to make informed, secure decisions in your organization. What you'll learn: 1. Understand foundational AI concepts and the unique security perimeter of machine learning models. 2. Identify common AI-specific vulnerabilities, including prompt injection, model extraction, and data poisoning. 3. Evaluate privacy risks associated with training data, intellectual property, and retrieval-augmented generation patterns. 4. Apply trust and safety frameworks to mitigate bias, hallucination, and ethical risks in AI outputs. 5. Design basic secure deployment workflows by integrating security best practices into the AI lifecycle. You will start by mastering core AI terminology and safety concepts before exploring real-world threat scenarios and mitigation strategies. Through clear written explanations and practical case studies, you will build a solid foundation in modern AI risk management. This course is designed for beginners, cybersecurity professionals transitioning to AI, and decision-makers looking to understand AI risks with no prior machine learning experience required. Start reading today to build safer, more resilient AI implementations.