Securing AI Data Pipelines: Fundamentals of AI Data Attacks

As artificial intelligence becomes central to modern software, securing the data that feeds these systems is more critical than ever. Traditional security measures often fall short when protecting complex AI pipelines from sophisticated data-centric exploits. This course provides a comprehensive introduction to the vulnerabilities inherent in AI data pipelines and teaches you how to recognize, analyze, and mitigate data-driven attacks. You will transition from understanding basic security concepts to identifying real-world threats like data poisoning and model manipulation. What you'll learn: Understand the core terminology of AI data pipeline security and common vulnerability points; Identify different types of data attacks, including data poisoning and adversarial manipulation; Analyze how prompt injection and input-based attacks exploit large language models; Explore data privacy risks, membership inference, and model extraction techniques; Apply best practices for securing data ingestion, preprocessing, and training phases; Evaluate modern defense strategies to build resilient, trustworthy AI systems. This text-based course begins with foundational concepts of AI architecture and data flows before guiding you through detailed explanations of specific attack vectors and modern defensive techniques. Designed for beginners in cybersecurity, software development, or data science, this course requires no prior experience in AI security. Start reading today to build a strong foundation in protecting AI systems from modern data threats.