AI Security Fundamentals: Protecting Intelligent Systems

As artificial intelligence becomes deeply integrated into modern software, securing these systems is no longer optional. Traditional cybersecurity measures are not enough to protect machine learning models from specialized attacks like prompt injection and data poisoning. This text-based course equips you with the foundational knowledge to identify vulnerabilities, assess risks, and implement secure practices throughout the AI lifecycle. You will transition from understanding basic security concepts to analyzing threat vectors unique to intelligent systems. What you'll learn: - Understand the fundamental security principles and terminology unique to artificial intelligence and machine learning. - Identify common vulnerabilities in large language models, including prompt injection and training data poisoning. - Analyze data privacy concerns and learn how to protect sensitive training data from leakage. - Apply basic threat modeling frameworks to assess risks in AI-driven applications. - Explore secure deployment strategies to safeguard models in production environments. You will start with essential terminology and the core differences between traditional software security and AI security. From there, the written modules guide you through data protection, model vulnerabilities, and modern defense frameworks, concluding with practical steps for secure deployment. Designed for beginners, this course requires no prior background in machine learning or cybersecurity, making it ideal for developers, project managers, and technology enthusiasts. Start reading today to build a secure foundation for the future of intelligent technology.