CIS 340: AI for Cybersecurity
Smart Defense for the Digital Business — Spring 2027 · Monday & Wednesday, 10:00–11:15 am, Rockwell West (RWW) 118.
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A hands-on, intuition-first introduction to using AI to defend the digital business — and to defending AI itself. No heavy math, no prior coding required. Each week pairs an interactive lecture with a guided Python lab in Google Colab.
This Semester at a Glance
| Week | Dates | Topic | Reading | Released / Due |
|---|---|---|---|---|
| 1 | Jan 12–16 | Welcome to Cybersecurity | Handout 1: Cybersecurity Fundamentals | Lab 1 released (Mon); Quiz 1 (Wed, in-class) |
| 2 | Jan 19–23 | Data, the Fuel of AI | Handout 2: Introduction to Data and AI | Lab 1 due (Mon); Lab 2 released (Mon); Quiz 2 (Wed) |
| 3 | Jan 26–30 | Supervised Learning: Teaching with Labels | Handout 3: Supervised Learning Basics | Lab 2 due (Mon); Lab 3 released (Mon); Quiz 3 (Wed) |
| 4 | Feb 2–6 | Regression and Model Evaluation | Handout 4: Regression and Model Metrics | Lab 3 due (Mon); Lab 4 released (Mon); Quiz 4 (Wed) |
| 5 | Feb 9–13 | Unsupervised Learning and Exam 1 | Handout 4: Regression and Model Metrics | Lab 4 due (Mon); Exam 1 (Wed, covers Weeks 1–4) |
| 6 | Feb 16–20 | Unsupervised Learning: Finding Hidden Patterns | Handout 5: Unsupervised Learning and Clustering | Lab 5 released (Mon); Quiz 5 (Wed) |
| 7 | Feb 23–27 | Semi-Supervised and Reinforcement Learning | Handout 6: Semi-Supervised and Reinforcement Learning | Lab 5 due (Mon); Lab 6 released (Mon); Quiz 6 (Wed) |
| 8 | Mar 1–5 | Threats from the Inside: How Attackers Use AI | Handout 7: AI-Powered Attacks | Lab 6 due (Mon); Lab 7 released (Mon); Quiz 7 (Wed) |
| 9 | Mar 8–12 | Spring Break and Exam 2 | — | Lab 7 due (Mon); Exam 2 (online, covers Weeks 5–8) |
| 10 | Mar 15–19 | Adversarial Examples and Data Poisoning | Handout 8: Adversarial Attacks on ML Systems | Lab 8 released (Mon); Quiz 8 (Wed) |
| 11 | Mar 22–26 | Prompt Injection and LLM Security | Handout 9: LLM Security and Prompt Injection | Lab 8 due (Mon); Lab 9 CTF released (Mon); Lab 9 due (Wed) |
| 12 | Mar 29–Apr 2 | AI Ethics, Bias, Fairness, and Governance | Handout 10: AI Ethics, Bias, and Fairness | Lab 10 released (Mon); Quiz 9 (Wed) |
| 13 | Apr 5–9 | Privacy in ML Pipelines and Trade-Offs, plus Exam 3 | Handout 11: Privacy-Preserving ML and Trade-Offs | Lab 10 due (Mon); Lab 11 released (Mon); Exam 3 (Wed); Quiz 10 (Wed) |
| 14 | Apr 12–16 | Trustworthy AI and Responsible AI in Practice | Handout 12: Trustworthy AI and Responsible AI | Lab 11 due (Mon); Lab 12 Tabletop released (Mon); Quiz 11 (Wed) |
| 15 | Apr 19–23 | Advanced Topics and Course Wrap-Up | Handout 13: Advanced Topics and Future Threats | Lab 12 due (Mon); Quiz 12 (Wed) |
| 16 | Apr 26–30 | Final Q&A and Exam Preparation | — | Review session (Mon and Wed) |
| 17 | May 3–7 | Final Exam Week | — | Exam 4 (covers Weeks 10–16, per university final-exam schedule) |
See the full schedule & readings →
Course Materials
Quizzes Locked
12 weekly quizzes (25%). Unlocked by the instructor or TA on quiz day.
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Heads up: Quizzes, labs, and exams stay locked
until the instructor or a TA releases them. A locked item shows in the list so
you can plan ahead, but the link only goes live once it is unlocked. Submit all
graded work in Canvas.
Learning Objectives
After completing this course, you will be able to:
- Explain core cybersecurity concepts, common threats, and defensive strategies — including the human factor.
- Describe the four major machine-learning styles and match each to business and security use cases.
- Build, run, and evaluate beginner ML models in Python (spam classifiers, fraud detectors, anomaly detectors).
- Interpret model results for decision makers (accuracy, false positives, operational trade-offs).
- Explain how attackers use AI and how AI systems are attacked (adversarial examples, poisoning, evasion, inversion, prompt injection).
- Apply basic prompt engineering and defensive prompting to LLM tools.
- Discuss AI ethics, bias and fairness, privacy, and major AI governance frameworks.
- Participate in beginner CTF and tabletop incident-response exercises on free industry platforms.