Foundations of AI Security: Guarding Models, Data, and Systems

As artificial intelligence becomes central to modern business, securing these systems against novel threats is more critical than ever. Organizations must balance the transformative power of AI with robust security protocols to protect sensitive data and maintain trust. This written course provides a foundational understanding of AI security, helping you recognize vulnerabilities unique to machine learning models and implement effective guardrails. You will transition from understanding basic AI concepts to evaluating risks and designing secure AI deployment strategies. What you'll learn: - Understand core AI security terminology and the unique threat landscape of machine learning systems. - Identify common vulnerabilities in Large Language Models (LLMs), including prompt injection and data poisoning. - Apply data protection principles to prevent sensitive data leakage during AI training and inference. - Evaluate compliance requirements and AI governance frameworks to ensure ethical and legal alignment. - Design secure architectures for AI integrations, including guardrails for retrieval-augmented generation pipelines. - Analyze real-world industry scenarios to proactively mitigate security risks in deployed AI applications. The course begins with foundational AI definitions and security concepts before moving into specific threat modeling, vulnerability analysis, and governance strategies. Through structured written lessons and practical scenarios, you will build a comprehensive blueprint for securing organizational AI. This course is designed for security professionals, data protection officers, IT managers, and business leaders who are new to AI security. No prior programming or machine learning experience is required. Start reading today to build a safer, more resilient foundation for your organization's AI initiatives.