Hardening AI Systems: Incident Response and Rapid Recovery

As artificial intelligence moves from experimental notebooks to production environments, securing and maintaining these systems becomes a critical priority. When an AI system fails or experiences a security breach, knowing how to quickly isolate, patch, and recover the system is essential to minimizing downtime and protecting data. This text-based course guides you through the foundational concepts of AI security, vulnerability patching, and incident response. You will transition from understanding basic machine learning hazards to actively designing resilient recovery workflows for modern AI pipelines. What you will learn: 1. Understand fundamental AI security risks, including prompt injection, data poisoning, and model inversion. 2. Configure basic monitoring and observability tools to detect anomalous model behaviors in real-time. 3. Apply patching strategies to secure retrieval-augmented generation (RAG) systems and vector databases. 4. Develop incident response playbooks to isolate compromised AI models and roll back to safe states. 5. Implement automated testing with modern MLOps tools to validate model integrity before redeployment. The course begins with core definitions of AI vulnerabilities and threat modeling before moving into step-by-step written tutorials on incident detection, containment strategies, and rollback procedures. You will practice these concepts through conceptual exercises and code-based scenarios designed to simulate real-world production failures. This course is designed for beginner DevOps engineers, system administrators, and aspiring MLOps professionals who want to understand AI-specific security and operational recovery. No prior experience with AI security is required. Start reading today to build the specialized skills needed to keep production AI systems secure, resilient, and highly available.