AI Security Monitoring and Incident Response (Self-Paced)

Self-paced online training with expert support

An AI system can be compromised without anyone laying a finger on the server it runs on. Learn what to watch, how to separate a real AI incident from a performance wobble, and what the first hour should look like.

$799 8 clock hours Live Support Start anytime

This course includes

  • 8 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 AI Security Monitoring and Incident Response.

A tightly scoped, practitioner-level course on the two places conventional IT monitoring and incident response fall short with AI: knowing what a compromise actually looks like, and knowing what to do once you see one. The idea holding it together is that an AI system carries every traditional attack surface and then piles on more, all bound up in data, models and inference behavior — which means an attacker can tamper with what a model learns from, or with what it is shown at run time, and never come near the infrastructure. Monitoring has to move with that, away from "is the system up" and toward "is the system behaving the way it was meant to, on data it was meant to see, for purposes it was approved for."

The practical core is a signals table that maps observed patterns to likely causes, paired with a first-hour triage sequence. A sustained spike in near-identical unusual inputs reads as adversarial probing; a slow accuracy decline reads as drift; an unexplained query spike from one credential reads as an extraction attempt. From there the course turns to judgment — separating anomalies from incidents, holding on to the evidence responders routinely lose (the model version in use, the prompt and response logs around the event, training data lineage, the guardrail configuration that was in effect), and grading severity by impact rather than technical difficulty. Its sharpest teaching point: a wrong eligibility or pricing decision caused by a data integrity failure is a high-severity incident even though nothing technically broke. The last module runs the four-phase incident response lifecycle with containment built for AI — rolling a model back to a validated version, switching automated decision-making off in favor of human review, quarantining a training data source that has been compromised, and shutting a confirmed prompt injection vector.

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This course has a prerequisite

Foundational cybersecurity knowledge. Familiarity with AI and machine learning terminology is helpful but not required.

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