# 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

1. **Cyber risk concepts and assets** — What is at risk, and how is it valued?
2. **Threat likelihood and impact modeling** — How can AI support risk estimation?
3. **Vulnerability prioritization** — Which weaknesses matter most now?
4. **Scenario analysis and stress testing** — How do we reason about uncertain attacks?
5. **Controls and residual risk** — How do mitigations change risk?
6. **Executive reporting and risk communication** — How should cyber risk be communicated to leaders?
7. **Governance, compliance, and audit** — How do AI risk tools support formal obligations?
8. **Cyber risk assessment portfolio** — What evidence supports risk decisions?