CAI - Cybersecurity and Artificial Intelligence

This lab-intensive course examines the statistical, data-based nature of AI vulnerabilities. Students will simulate evasion attacks where input data is subtly tweaked to fool a model into an incorrect prediction and data poisoning where manipulated data is inserted into training sets.

Focuses on building security into the ML lifecycle through AISecOps and MLSecOps. Topics include AI bills of materials (BOMs) and least-privilege for AI assistants.

Leverages machine learning for cyber incident attribution and predictive threat analysis, including AI-driven darknet intelligence.

This course is designed to advance learners from manual triage to automated response. Using Splunk SOAR, they design and execute playbooks that orchestrate tools and
streamline response actions; with Enterprise Security, they tune correlation searches, build dashboards, and apply risk-based alerting for prioritized detections. The capstone synthesizes skills into a portfolio ready SOC project—complete with runbooks, metrics, and an executive briefing—while preparing learners for the Splunk SOAR User certification.

Explores the use of AI to orchestrate Zero Trust Network Access (ZTNA). Students learn to apply AI-centric, identity-based controls that replace implicit network trust with dynamic, real-time access decisions. 

Focuses on building automated forensics pipelines. Students utilize machine learning for artifact classification, timeline reconstruction, and anomaly reporting to accelerate highstakes investigations.

This course investigates the attack surface of Agentic AI in corporate and government environments.This course is particularly relevant given the rise of Retrieval-Augmented Generation (RAG) systems, which connect LLMs to sensitive corporate databases. Students will conduct research and work through the following phases: threat modeling and scenario design, execution of adversarial attacks, and implementation of defensive countermeasures.