Cybersecurity Innovation for Cyberinfrastructure
This grant supports projects that improve cybersecurity for scientific research infrastructure, focusing on usability, data, resilience, and AI data integrity.
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Funder: U.S. National Science Foundation
Due Dates: April 2, 2025 | January 21, 2026 | January 20, 2027
Funding Amounts: Up to $1,200,000 per award (up to 3 years); total program funding $8M–$12M; 12–20 awards expected.
Summary: Supports applied research, development, and deployment of cybersecurity solutions to enhance the security, privacy, and resilience of scientific cyberinfrastructure.
Key Information: Limit of 2 proposals per individual as PI/co-PI/key personnel; no cost sharing required.
Description
This program aims to advance scientific discovery by improving the security, privacy, and robustness of cyberinfrastructure critical to research across disciplines. The initiative supports the development, deployment, and integration of cybersecurity solutions tailored to the unique needs of scientific data, computation, collaboration workflows, and infrastructure. Proposals must address the specific requirements of scientific cyberinfrastructure and are expected to benefit the broader scientific community.
The program solicits proposals in four focus areas:
- Usable and Collaborative Security for Science (UCSS): Research on security and usability that facilitates scientific collaboration and integrates security into scientific workflows.
- Reference Scientific Security Datasets (RSSD): Creation and dissemination of reference datasets from scientific cyberinfrastructure to support reproducible security research.
- Transition to Cyberinfrastructure Resilience (TCR): Efforts to improve the resilience, robustness, and trustworthiness of scientific cyberinfrastructure, including technology transition activities.
- Integrity, Provenance, and Authenticity for AI Ready Data (IPAAI): Projects enhancing the integrity, provenance, and authenticity of scientific datasets used in AI-driven research.
Key program expectations include open sharing of datasets following FAIR principles, explicit partnerships with domain scientists or IT organizations, and sustainability planning.
