Secure AI Research Foundations

As AI systems become more prevalent, ensuring their security and ethical integrity from the ground up is paramount to prevent vulnerabilities and build user trust. This course provides the foundational understanding and practical methods to embed security principles into your AI projects, leading to more resilient and compliant systems. You will gain the knowledge to identify, mitigate, and communicate AI-specific security risks throughout the research and development lifecycle. What you'll learn: Understand fundamental principles of AI security and responsible AI development. Identify and analyze AI-specific adversarial threats using frameworks like OWASP LLM Top 10. Implement robust data protection and privacy controls for AI training and inference. Design secure architectures and integration patterns for deploying AI models. Evaluate AI system vulnerabilities and communicate security risks to technical and non-technical stakeholders. Apply security best practices to prompt engineering and Retrieval Augmented Generation (RAG) patterns. Explore foundational MLOps security considerations for the AI system lifecycle. The course begins by establishing core security concepts and their relevance to AI, then progresses to practical methods for risk assessment, data protection, and secure deployment. You will read through foundational concepts, explore real-world attack vectors, and learn to apply best practices for building secure and trustworthy AI systems from the ground up. This course is designed for AI researchers, developers, data scientists, and anyone interested in understanding and implementing security measures for AI systems, with no prior security expertise required. Begin your journey to building more secure and responsible AI today.