Centralized vs. Chargeback IT Funding: Reducing Technical Debt and Cyber Risk in Higher Education

In an era of escalating cyberthreats, constrained budgets, and rapid AI adoption, higher education IT leaders face critical governance decisions on resource allocation that directly impact institutional risk and resilience. This session presents empirical findings from a Michigan State University ITM PhD dissertation analyzing centralized vs. chargeback IT funding models and their effects on technical debt across infrastructure, security patching, and AI readiness in U.S. public higher ed. From anonymized data on 20 diverse institutions (10 each model; balanced by size/region/Carnegie class; EDUCAUSE 2024/NIST-aligned), a novel 3D optimization framework maps performance to an optimal zone (ITD: 3? 4 yrs.; SPD: 4? 5 patches; AITD: 5? 10 score). Welch t-tests reject H0 (p<0.001 ITD/SPD, p=0.007 AITD): centralized yields mean Euclidean distance 0.40 vs. 1.30 chargeback (95% CIs: ITD 1.33 [0.93,1.73]; SPD 1.12 [0.73,1.51]; AITD 0.82 [0.19,1.45]; robust to OLS controls for enrollment/R1). 3D visualizations reveal centralized pooling has advantage in optimization of resources. Attendees gain: (1) Framework Excel/LaTeX tools to compute/map own debts. (2) Data-backed governance recs: centralize for risk reduction or coordinate chargeback. (3) Benchmarks for policy (scalable to 1,600 publics). For CIOs/risk officers seeking quantifiable GRC strategies. Optimize funding and mitigate debt now.