Harden AI: Secure Your ML Pipelines

Imagine deploying a powerful machine learning model that performs flawlessly—until a single unpatched container, a poisoned dependency, or a misconfigured cloud service brings it crashing down. In today’s AI-driven world, securing ML systems is no longer optional; it’s essential to maintaining trust, compliance, and resilience. Harden AI: Secure Your ML Pipelines is an intermediate, scenario-driven cybersecurity and AI governance course that immerses learners in the realities of protecting machine learning infrastructure. Through a blend of theory sessions, guided demonstrations, and AI-assisted coach dialogues, participants explore how to harden ML environments, secure CI/CD workflows, and build resilient pipelines that can withstand compromise. Real-world case studies—ranging from exposed Jupyter notebooks to supply chain attacks and model drift—anchor the learning experience in practical relevance. This course is for ML engineers, DevOps professionals, and AI practitioners who want to secure their ML pipelines. It also suits data scientists and developers managing AI systems in cloud or containerised environments. Learners should have basic knowledge of ML workflows, cloud or container security, and general awareness of cyber threats. By the end of the course, learners will have developed a security-by-design mindset, equipped with both the technical skills and ethical awareness to deploy trustworthy, compliant, and resilient AI systems in real-world environments.