AINS6302: AI for Risk Assessment#
Aurnova MSAI track: Cybersecurity AI
Credits: 3
Format: 8-week online graduate course
Models cyber risk using assets, likelihood, impact, vulnerability prioritization, controls, reporting, and audit evidence.
This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.
Course Outcomes#
By the end of the course, students will be able to:
explain the major concepts and tradeoffs in AI for Risk Assessment;
build or evaluate applied AI artifacts aligned with the course domain;
document assumptions, evidence, limitations, and operational risks;
connect technical work to governance, stakeholder needs, and deployment readiness.
Module Map#
Cyber risk concepts and assets — What is at risk, and how is it valued?
Threat likelihood and impact modeling — How can AI support risk estimation?
Vulnerability prioritization — Which weaknesses matter most now?
Scenario analysis and stress testing — How do we reason about uncertain attacks?
Controls and residual risk — How do mitigations change risk?
Executive reporting and risk communication — How should cyber risk be communicated to leaders?
Governance, compliance, and audit — How do AI risk tools support formal obligations?
Cyber risk assessment portfolio — What evidence supports risk decisions?