Built for End to End Practioner Use

Bulit and Instructed by David Mosher, MS (Cybersecurity) and currently a PhD candidate (Cybersecurity Management)

5 Learning Objectives

Course goals

1. Design and defend a tiered AI risk assessment for a real system using the NIST AI RMF, ISO/IEC 42001, and EU AI Act frameworks as complementary references.

2. Distinguish AI-specific risks (bias, hallucination, prompt injection, model drift, supply-chain opacity) from traditional cybersecurity risks, and apply the right control set to each.

3. Evaluate vendor and LLM supply-chain exposures using structured criteria (data residency, model versioning, sub-processor visibility, right-to-audit).

4. Produce the four artifacts a functioning AI risk program requires: a tiered inventory, a bias-test plan, a POA&M, and a board-level summary.

5. Operate an AI Management System aligned to ISO/IEC 42001 that keeps pace with regulatory change and continues to protect the organization as the AI portfolio grows.

Course curriculum

    1. Foundations of AI Risk

      FREE PREVIEW

    2. APA7 Reference List

    1. AI Governance and Regulation

    1. Course Activity Excel workbook

    2. Risk Identification and Tiering

    1. Bias, Fairness, and Explainability

    1. Security, Adversarial ML, and Generative AI

    1. Human Oversight and Accountability

About this course

  • $797.00
  • 2 hours of video content