CICI: UCSS: Securing Research Cyberinfrastructures with Zero Trust and Agentic AI

Research Cyberinfrastructure (RCI) supports cutting-edge scientific projects benefitting individuals, the environment, national security, and economic competitiveness. Despite the potential benefits, recent attacks have compromised the integrity of scientific data and resources such as computing time and network bandwidth, ultimately affecting the public’s confidence in scientific results. As a response, countermeasures have been proposed based on Zero Trust (ZT), an emerging paradigm calling for the continuous evaluation and enforcement of authorization policies, which protect RCIs by restricting access to sensitive data and resources. However, it is difficult for practitioners of RCIs, namely, administrators, researchers, and students, to correctly write, evaluate, and enforce the many different authorization policies needed to provide effective cyber-protections. To address these challenges, this project develops ZT-Agents, which leverage Agentic Artificial Intelligence (AI) techniques to provide next-generation cyber-protections for RCIs, ultimately pursuing the following goals: (i) Advancing the state-of-the-art in cyber-protections by developing AI agents for continuous evaluation and enforcement of authorization policies for RCIs; (ii) Enabling practitioners to conveniently learn and retrofit Agentic AI-augmented authorization technologies for RCIs, balancing security, usability, and the needs of research-driven enterprises; (iii) Providing evidence-backed technical recommendations and best practices on ZT-inspired continuous authorization technologies for RCIs, thus favoring efficient implementations in practice; (iv) Disseminating next-generation cybersecurity and Agentic AI solutions via hands-on challenges to students in College and K-12 levels, as well as to practitioners of RCIs; and, (v) Disseminating the results of this project via publications in well-reputed scientific venues as well as articles and presentations for public audiences. 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. NSF Award ID: 2613459 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Carlos Rubio-Medrano | Institution: Texas A&M University Corpus Christi, CORPUS CHRISTI, TX | Award Amount: $599,958 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2613459 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2613459.html