Overview

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.

Syllabus

Infrastructure Hardening for ML

This module lays the foundation for securing machine learning systems by focusing on the underlying infrastructure that supports them. Learners will explore why strong security controls at the operating system, cloud, and container levels are essential for protecting sensitive ML workloads. Real-world breaches often start with overlooked vulnerabilities in servers, misconfigured storage buckets, or unsecured APIs, and this module provides the knowledge to prevent such entry points. Through theory, demonstration, and an interactive scenario, learners will gain the skills to harden ML environments, apply IAM best practices, and perform vulnerability scans that reveal weaknesses before attackers exploit them. By the end of this module, learners will understand how infrastructure hygiene directly impacts the integrity of ML models and data.

Securing ML CI/CD Pipelines

This module builds on the infrastructure layer by addressing the unique risks found in machine learning build and deployment workflows. Continuous integration and continuous deployment (CI/CD) pipelines accelerate innovation, but they also introduce opportunities for adversaries to slip in malicious dependencies, poisoned data, or corrupted artifacts. Learners will study the anatomy of ML supply chain attacks and discover practical strategies to counter them, such as dependency scanning, code signing, and reproducible builds. The combination of theory, real-world case studies, and a hands-on demo will help learners see how insecure workflows can compromise entire AI systems. By the end of this module, participants will be able to design and implement CI/CD pipelines that embed security into every stage of model development and deployment.

Building Resilient ML Pipelines

This module brings together infrastructure and workflow security into a forward-looking focus on resilience. No pipeline is immune to compromise or error, but resilient pipelines are designed to detect issues quickly, recover gracefully, and maintain trustworthiness under stress. Learners will study common compromise vectors in ML systems, from adversarial inputs to model drift, and then explore resilience strategies like rollback, redundancy, and drift monitoring. The demo illustrates how even a simple rollback can protect business continuity when a model misbehaves in production. The scenario-based dialogue challenges learners to think critically about balancing speed, reliability, and safety in real-world ML operations. By the end of this module, learners will understand how to engineer resilience into ML pipelines so that failures and attacks become manageable events rather than catastrophic disruptions.