Jiao (Aimee) Wu
Associate Professor
Department of Operations Management & Information Systems
College of Business
Course(s) Targeted by this Innovation
OMIS 460 & 660: Business Data Networks and Cybersecurity
Development and Implementation
2026-2027
Purpose and Impact
OMIS-460 (undergraduate) and OMIS-660 (graduate), Business Networking and Cybersecurity, are high-impact courses with enrollments of 30–45 students that serve both as an OMIS elective and a required course in the recently announced Master of Science in Artificial Intelligence for Business (MSAIB) program. While they provide a strong foundation in networking and traditional cybersecurity, they do not yet fully address the rapidly emerging risks introduced by AI systems.
As organizations increasingly adopt AI, new security threats, such as prompt injection, adversarial data manipulation, and vulnerabilities in AI infrastructure, are becoming urgent real-world challenges. There is a growing demand for professionals who can secure, audit, and control AI systems, not just use them.
This redesign integrates AI cybersecurity into the existing curriculum, enabling students to understand and mitigate AI-related risks using frameworks such as NIST and emerging AI governance principles. By focusing on AI security, auditing, and red teaming, the course will develop critical thinking and decision-making skills aligned with industry needs.
The outcome is a more engaging, future-ready learning experience that prepares students for high-demand roles such as AI security analyst and AI auditor, while supporting industry certifications (e.g., CompTIA Security+, CISSP) and enhancing workforce readiness.
Description of Innovation
The proposed innovation integrates AI cybersecurity concepts into the existing course structure through redesigned assignments, case-based learning, and scenario-driven activities. The course positions AI as a system that must be secured, audited, and controlled within business network environments. Core networking topics (e.g., network architecture, LANs, TCP/IP, and network management) will be extended to include AI-related vulnerabilities, such as prompt injection, adversarial data manipulation, data leakage, and weaknesses in AI infrastructure. Students will analyze how AI systems introduce new attack surfaces and apply structured frameworks (e.g., NIST) to identify and mitigate risks.
Key learning activities will include a series of AI red team vs. blue team simulations, in which students can design and defend against AI-based attacks, and an AI audit and diagnosis assignment, in which students evaluate system vulnerabilities and propose mitigation strategies. Additional case analyses and mini-labs will expose students to real-world AI security incidents and adversarial scenarios. Across all activities, students will be guided to critically evaluate AI tools and systems, emphasizing responsible AI governance and avoiding overreliance.
These modules and components can be implemented in both face-to-face and online formats and across undergraduate and graduate levels, ensuring flexibility, scalability, and strong alignment with emerging workforce demands in AI security.