AI Endpoint Security: Threat Modeling & Testing

As AI models are increasingly deployed in production, securing their inference endpoints against sophisticated attacks is a critical skill. This course provides a foundational understanding of AI security, enabling you to proactively protect your AI systems. By the end of this course, you will possess the knowledge and practical skills to perform threat modeling, conduct security testing, and implement robust defenses for AI inference endpoints, ensuring the integrity and reliability of your AI applications. What you'll learn: * Understand fundamental concepts of AI security and common attack surfaces. * Apply systematic threat modeling methodologies to identify risks in AI inference endpoints. * Practice identifying potential attack vectors specific to AI, including basic prompt injection. * Implement effective testing strategies to discover and validate security flaws in AI systems. * Learn about common mitigation techniques and best practices for securing AI deployments. * Explore principles of secure MLOps practices for protecting AI endpoints throughout their lifecycle. This course begins by defining core AI security terminology and concepts, then progresses through practical threat modeling frameworks and hands-on testing techniques, concluding with strategies for building more secure AI endpoints. This course is for beginners in AI development, cybersecurity, or MLOps who want to understand and implement security best practices for AI inference endpoints. No prior security expertise is required. Start your journey to securing the future of AI today.