Collaborative Research: Promoting Undergraduate Discovery in Cybersecurity for Multi-Cloud Machine Learning Operations

This project aims to serve the national interest by improving how undergraduate students learn to secure machine learning systems that operate across multiple cloud providers. These systems increasingly support critical infrastructure, public services, business operations, and scientific discovery, yet undergraduate education often teaches cloud computing, machine learning, and cybersecurity as separate subjects. As a result, students may not develop the integrated reasoning needed to understand how security failures can emerge when data, models, software, identities, and cloud services interact. The significance of this Level 1 Engaged Student Learning project is that it intends to help students build a practical, transferable understanding of cybersecurity in modern artificial intelligence systems. Project Nimbus plans to develop and disseminate five portable, hands-on instructional modules supported by a practice-oriented security sandbox. The sandbox will include both a free, open-source version and a commercial cloud version, lowering adoption barriers for community colleges, minority-serving institutions, and universities with different levels of technical infrastructure. By expanding access to realistic learning experiences, the project intends to strengthen the national workforce prepared to secure artificial intelligence and machine learning systems. Project Nimbus goals and scope center on developing, piloting, and studying five self-contained modules that integrate multi-cloud computing, machine learning operations, and cybersecurity through inquiry-based learning at the Rochester Institute of Technology and the University of Alabama. Guided by Situated Learning and the 5E instructional model (Engage, Explore, Explain, Elaborate, and Evaluate), students will investigate “broken-by-design” machine learning pipelines, perform structured threat modeling, and practice red-team, blue-team, and purple-team defense in realistic cloud security scenarios. Project Nimbus plans to study the effectiveness of this learning environment using a mixed-methods design that examines students’ transfer of security reasoning across unfamiliar cloud architectures, as well as changes in cybersecurity self-efficacy, belonging, and professional identity. Data sources will include pre- and post-concept measures, rubric-scored performance artifacts, structured reflections, think-aloud protocols, and implementation feedback from instructors and students. The results of Project Nimbus will be disseminated through open instructional modules, instructor guides, assessment rubrics, sandbox setup materials, faculty development workshops, and cybersecurity education repositories. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.