As artificial intelligence becomes embedded across teaching, administration, research, and student services, higher education institutions face increasing pressure to secure AI systems while enabling innovation. AI security extends beyond technical controls and requires coordinated governance, risk management, and shared responsibility across the institution.

This course provides a practical, role inclusive overview of AI security in higher education, grounded in three interconnected domains: AI governance and program management, AI risk management, and AI technologies and controls. Participants will learn how institutions can establish clear ownership for AI decisions, align AI use with compliance and ethical obligations, and reduce risk from fragmented or shadow AI deployments.

The presentation examines real world implications of AI adoption for faculty, staff, administrators, and IT leaders, including data privacy risks, bias and equity considerations, vendor risk, and misuse of generative AI tools. Rather than focusing solely on technical defenses, the session highlights how governance structures, operational guardrails, and everyday decisions across roles contribute to institutional trust and responsible AI adoption.

Learning Objectives
1. Understand the core components of AI security in higher education, including governance, risk management, and technical controls, and how they intersect across institutional functions.
2. Identify common AI related risks affecting students, faculty, staff, and institutional reputation, including data privacy, bias, misuse, and vendor related exposure.
3. Learn how different campus roles contribute to AI security through policy, oversight, and daily practices, not just technical implementation.
4. Apply practical strategies for establishing AI guardrails that enable innovation while maintaining compliance, trust, and accountability.