Securing AI/ML Systems: Protecting the Intelligent Attack Surface (Self-Paced)

Self-paced online training with expert support

AI breaks in ways ordinary software does not — corrupted before training, misled once deployed, or persuaded to reveal the data behind it. Work the entire surface at your own pace, from training set to prompt.

$1,049 12 clock hours Live Support Start anytime

This course includes

  • 12 hours of project-based training from experts
  • Answers from real instructors
  • Retake for free within 1 year
  • Access to class recordings (30 days)
  • Verified digital certificate of completion

This is the self-paced version. For live instruction, enroll in Securing AI/ML Systems: Protecting the Intelligent Attack Surface.

Treat your AI and machine learning deployments as systems that have to be defended, not as a governance checkbox. This self-paced course walks the data, the model, the pipeline and the application in turn, beginning with the line between ordinary cybersecurity exposure and the exposure that belongs to AI alone, then tracing the lifecycle from first design decision to retirement and marking where a security gate belongs. A full module is given over to adversarial machine learning: how training data gets poisoned, how deployed models get fooled, how privacy attacks pull training information back out, and how models get stolen outright — each paired with the defenses that make the system harder to move against, all keyed to the NIST adversarial machine learning taxonomy, AI 100-2 E2025. Generative AI is not an afterthought here but a module of its own, working through direct and indirect prompt injection, sensitive information disclosure, insecure output handling and the damage that flows downstream, plus everything that retrieval-augmented generation, plugins, tools and outside data sources drag in with them.

Two more modules go where most training simply does not. One reframes trained models, weights and checkpoints as assets that need provenance checks and change control of their own. The other confronts the AI supply chain, where a third-party model, dataset, library or framework is a dependency like any other and can be just as malicious or just as broken — a real problem, given how casually teams pull weights off public repositories compared with how they vet a software package. A closing workshop puts you in front of a fictional AI system to threat-model, with a prioritized hardening plan to argue for. This is built for whoever has to secure the thing, not whoever has to write the policy about it; if you want the awareness tier first, start with AI Security Fundamentals for the Cyber Workforce (Self-Paced).

Read more

This course has a prerequisite

Foundational knowledge of AI and machine learning systems.

Yelp Facebook LinkedIn YouTube Twitter Instagram