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Cybersecurity Management · Proposed course

Quantitative Financial Management in Cybersecurity

Course code
CSM 603
Credit hours
3
Program
MSCM
Delivery
Hybrid/Online

Course description

Course overview

This course provides an applied data analytics approach to financial management within cybersecurity operations. Students will learn quantitative methods for cybersecurity budgeting, cost-benefit analysis, and risk forecasting. Using analytical tools such as Python, R, and Power BI, students will model financial risks, estimate losses from cyber incidents, and develop strategies for optimizing cybersecurity investments and resources.

Course content is presented for institutional review and remains subject to approval and revision.

Academic focus

Course objectives

  1. 01

    Understand financial and economic principles relevant to cybersecurity investment and management.

  2. 02

    Apply quantitative tools and analytics to evaluate cybersecurity costs, risks, and ROI.

  3. 03

    Use data analytics software to model loss expectancy, incident cost, and budget optimization.

  4. 04

    Develop data-driven financial strategies to support cybersecurity and enterprise resilience.

Learning outcomes

Upon completion

  1. CLO 1

    Apply quantitative and statistical models to assess cybersecurity financial risks (maps to PLO 2, PLO 3).

  2. CLO 2

    Evaluate cybersecurity investments using ROI, NPV, and cost-benefit analysis (maps to PLO 2, PLO 5).

  3. CLO 3

    Develop and visualize financial risk dashboards using Power BI or Python (maps to PLO 3, PLO 6).

  4. CLO 4

    Design data-driven financial strategies that enhance risk mitigation and business continuity (maps to PLO 2, PLO 5).

Proposed syllabus

Weekly course schedule

The sequence below reflects the current 15-week syllabus and remains subject to institutional review.

  1. Week 1

    Introduction to Cybersecurity Finance and Analytics

  2. Week 2

    Foundations of Cyber Risk Economics and Cost Modeling

  3. Week 3

    Time Value of Money and ROI in Cybersecurity Projects

  4. Week 4

    Quantitative Methods for Risk Forecasting and Budgeting

  5. Week 5

    Single Loss Expectancy (SLE) and Annualized Loss Expectancy (ALE) Calculations

  6. Week 6

    Monte Carlo Simulations for Cyber Risk Prediction

  7. Week 7

    Financial Data Visualization with Power BI and Python

  8. Week 8

    Midterm Exam and Budget Optimization Case Study

  9. Week 9

    Cost-Benefit and Cost-Effectiveness Analysis in Cybersecurity

  10. Week 10

    Economic Impact of Data Breaches and Incident Response

  11. Week 11

    Cyber Insurance, Risk Transfer, and Return on Security Investment

  12. Week 12

    Data Analytics for Predictive Budgeting

  13. Week 13

    Strategic Financial Decision-Making in Cyber Operations

  14. Week 14

    Final Project Presentations

  15. Week 15

    Final Exam and Course Wrap-Up

Evaluation

Assessment and grading

  • Cyber Risk Analytics Project – 30%
  • Midterm Exam – 20%
  • Final Exam – 20%
  • Applied Financial Modeling Exercises – 20%
  • Participation and Professional Engagement – 10%
A = 90–100%B = 80–89%C = 70–79%F = Below 70%

Course resources

Required texts and materials

  • Anderson, R. (2020). Security engineering: A guide to building dependable distributed systems (3rd ed.). Wiley.
  • Hubbard, D. W., & Seiersen, R. (2016). How to measure anything in cybersecurity risk. Wiley.
  • Supplemental datasets, Excel models, and Power BI dashboards will be available through the LMS.