CYB 251 – Securing the AI/ML Infrastructure (NEW)

Course Overview

As AI and machine learning systems become increasingly integrated into modern infrastructure, understanding how to secure these environments is critical. This course is designed to help cybersecurity professionals navigate the unique risks and challenges of securing AI/ML systems.

Learners will explore key threats to AI/ML infrastructure, including adversarial attacks, data poisoning, and model manipulation, and gain hands-on strategies for protecting models, training data, and inference pipelines. In addition to technical safeguards, the course addresses governance, compliance, and ethical concerns, equipping learners with a complete security perspective across the AI lifecycle.

By the end of this course, learners will have the knowledge and skills to:

• Identify key security threats unique to AI/ML infrastructure.
• Understand risks such as adversarial attacks, data poisoning, and model manipulation.
• Implement best practices to secure AI models, training data, and inference pipelines.
• Recognize governance, compliance, and ethical considerations in AI security.
• Apply security frameworks and tools to protect AI/ML environments.