Cybersecurity Innovation for Cyberinfrastructure (CICI)

Cybersecurity Innovation for Cyberinfrastructure (CICI) is sponsored by NSF CISE Division of Computer and Network Systems. Advances scientific discovery by enhancing the security and privacy of cyberinfrastructure, focusing on AI-ready data integrity and authenticity.

NSF 25-531: Cybersecurity Innovation for Cyberinfrastructure (CICI) | NSF - U.S. National Science Foundation Active funding opportunity This document is the current version. Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements .

All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects. NSF 25-531: Cybersecurity Innovation for Cyberinfrastructure (CICI) To save a PDF of this solicitation, select Print to PDF in your browser's print options.

Program Solicitation NSF 25-531 U.S. National Science Foundation Directorate for Computer and Information Science and Engineering Office of Advanced Cyberinfrastructure Full Proposal Deadline(s) (due by 5 p. m.

submitting organization's local time): Third Wednesday in January, Annually Thereafter Important Information And Revision Notes The CICI program continues to support work that enables scientists and scientific discovery by improving the security, robustness, and trustworthiness of cyberinfrastructure.

The current solicitation: Clarifies the requirement to target and benefit scientific cyberinfrastructure, users, and applications, and identify unique security requirements of the cyberinfrastructure or science application that motivates the approach; and Adds a requirement that any curated datasets are required to be shared publicly and made available through established community platforms and adhere to the Findability, Accessibility, Interoperability, and Reuse of digital assets ( https://www.

go-fair. org/fair-principles/ ) Guiding Principles for scientific data management and stewardship. Adds a fourth track focused on integrity, provenance, and authenticity for scientific datasets used in Artificial Intelligence.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted.

The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements Cybersecurity Innovation for Cyberinfrastructure (CICI) The objective of the Cybersecurity Innovation for Cyberinfrastructure (CICI) program is to advance scientific discovery and innovation by enhancing the security and privacy of cyberinfrastructure.

CICI supports efforts to develop, deploy and integrate cybersecurity that will benefit the broader scientific community by securing science data, computation, collaborations workflows, and infrastructure.

CICI recognizes the unique nature of modern, complex, data-driven, distributed, rapid, and collaborative science and the breadth of infrastructure and requirements across scientific disciplines, practitioners, researchers, and projects.

CICI seeks proposals in four program areas: Usable and Collaborative Security for Science (UCSS): Projects in this program area should support novel and/or applied security and usability research that facilitates scientific collaboration, encourages the adoption of security into the scientific workflow, and helps create a holistic, integrated security environment that spans the entire scientific cyberinfrastructure ecosystem.

Reference Scientific Security Datasets (RSSD): Projects in this program area should leverage instrumented cyberinfrastructure to capture metadata from scientific workflows and workloads as reference data artifacts that can help support reproducible security research, testing and evaluation.

Transition to Cyberinfrastructure Resilience (TCR): Projects in this program area should improve the robustness, trustworthiness, integrity, and/or resilience of scientific cyberinfrastructure through testing, evaluation, hardening, validation, and technology transition of novel cybersecurity research. The TCR area further encourages transition activities that advance the deployment and use of reproducibility in CI, workflows, and data.

Integrity, Provenance, and Authenticity for Artificial Intelligence Ready Data (IPAAI): Projects in this program area should enhance confidence and reproducibility in AI produced scientific results by improving the integrity, provenance, and authenticity of scientific datasets used by Artificial Intelligence systems.

Broadening Participation In STEM NSF has a mandate to broaden participation in science and engineering, as articulated and reaffirmed in law since 1950. Congress has charged NSF to "develop intellectual capital, both people and ideas, with particular emphasis on groups and regions that traditionally have not participated fully in science, mathematics, and engineering."

Cognizant Program Officer(s): Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact. Daniel F.

Massey, telephone: (703) 292-5147, email: dmassey@nsf. gov Kevin Thompson, Program Director, CISE/OAC, telephone: (703) 292-4220, email: kthompso@nsf. gov Applicable Catalog of Federal Domestic Assistance (CFDA) Number(s): 47.

070 --- Computer and Information Science and Engineering Anticipated Type of Award: Standard Grant or Continuing Grant Estimated Number of Awards: 12 to 20 The estimated number of awards per program area is as follows: 3-6 Usable and Collaborative Security for Science (UCSS) awards; 2-5 Reference Scientific Security Dataset (RSSD) awards; 2-4 Transition to Cyberinfrastructure Resilience (TCR) awards; and 4-6 Integrity, Provenance, and Authenticity for Artificial Intelligence Ready Data (IPAAI) awards.

Anticipated Funding Amount: $8,000,000 to $12,000,000 Total funding for the CICI program is $8,000,000 to $12,000,000, subject to the availability of funds.

Each program area will support awards pursuant to the following budget and duration: Usable and Collaborative Security for Science (UCSS) awards will be supported at up to $600,000 total per award for up to 3 years; Reference Scientific Security Datasets (RSSD) awards will be supported at up to $600,000 total per award for up to 3 years; Transition to Cyberinfrastructure Resilience (TCR) awards will be supported at up to $1,200,000 total per award for up to 3 years; Integrity, Provenance, and Authenticity for Artificial Intelligence Ready Data (IPAAI)awards will be supported at up to $900,000 total per award for up to 3 years.

Who May Submit Proposals: Proposals may only be submitted by the following: Institutions of Higher Education (IHEs): Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the U.S., acting on behalf of their faculty members.

Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of sub-awards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the U.S. campus.

Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. There are no restrictions or limits. Limit on Number of Proposals per Organization: There are no restrictions or limits.

Limit on Number of Proposals per PI or co-PI: An individual can participate as PI, co-PI or senior/key personnel on no more than two CICI proposals. Note that any individual whose biographical sketch is provided as part of the proposal will be considered as Senior/Key Personnel in the proposed activity, irrespective of whether that individual would receive financial support from the project.

In the event that any individual exceeds this limit, any proposal submitted to this solicitation with this individual listed as PI, co-PI, or Senior/Key Personnel after the second proposal is received at NSF will be returned without review. No exceptions will be made. Proposal Preparation and Submission Instructions A.

Proposal Preparation Instructions Letters of Intent: Not required Preliminary Proposal Submission: Not required Full Proposals submitted via Research. gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=pappg . Full Proposals submitted via Grants. gov: NSF Grants.

gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants. gov guidelines apply (Note: The NSF Grants. gov Application Guide is available on the Grants.

gov website and on the NSF website at: https://www. nsf. gov/publications/pub_summ.

jsp? ods_key=grantsgovguide ). Cost Sharing Requirements: Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations: Other Budgetary Limitations: Full Proposal Deadline(s) (due by 5 p. m. submitting organization's local time): Third Wednesday in January, Annually Thereafter Proposal Review Information Criteria National Science Board approved criteria.

Additional merit review criteria apply. Please see the full text of this solicitation for further information. Award Administration Information Additional award conditions apply.

Please see the full text of this solicitation for further information. Standard NSF reporting requirements apply. Scientific research cyberinfrastructure (CI), including computing, networking, data, and Artificial Intelligence (AI), plays a central role in supporting collaborative, data-driven discovery across all scientific disciplines.

However, science CI faces unique security and privacy challenges. Collaboration and resource sharing are integral to open science but must adhere to security policies and regulations while providing as seamless of a workflow as possible to domain scientist users. Simultaneously, data and workflow integrity, provenance, and authenticity are crucial to the scientific enterprise and the reproducibility of scientific results.

Both unintentional and malicious cyberinfrastructure errors can lead to invalid results and this risk only increases due to growing adoption and use of AI systems. This solicitation broadly targets improving research CI through cybersecurity, thereby creating CI that facilitates scientific initiatives, projects, users, collaborations, and discoveries.

Cyberinfrastructure (CI) plays a key role in modern scientific exploration and discovery. CI has become an integral enabler of research across disciplines as the amount of computationally accessible scientific data grows exponentially. Secure and robust scientific infrastructure is thus vital for multiple stakeholders.

Operators wish to protect their infrastructure from misuse, ensure high availability, and avoid liability. Policy makers seek to promote science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles while ensuring that sensitive research data (e.g., personally identifiable information or intellectual property), cannot be ex-filtrated or abused.

The research community and public must maintain their confidence in the integrity and authenticity of the entire research process; this necessitates transparency and reproducibility along every step of the computational workflow(s) to ensure rigorous science and trust in the results.

Further, the growing use of AI systems as part of the scientific process amplifies both the speed at which scientific data is analyzed and the importance of data integrity, provenance, and authenticity. Domain scientists require performant and available cyberinfrastructure. However, end-users of open scientific infrastructure may consider security processes valuable only insofar as they do not slow or otherwise impede their research.

Ensuring the usability of security mechanisms is therefore critical to their adoption and use within the scientific community. Scientific data and workloads can be fundamentally different from those seen in traditional network, storage, and computing scenarios. Individual platforms, projects, and data may have significantly different security sensitivities, threats, and constraints.

Similarly, scientific CI applications frequently employ unique hardware, software, and configurations that may be unmaintained, less well-vetted, or introduce entirely new classes of vulnerabilities. Solutions to protecting scientific data, computation, and workflows must thus both balance and expose these trade-offs, all while accommodating a variety of policies and stakeholders.

The objective of the Cybersecurity Innovation for Cyberinfrastructure (CICI) program is to develop, integrate, and transition cybersecurity, privacy, and usability solutions that benefit cyberinfrastructure and the wider scientific community.

This solicitation seeks research to make scientific data, workflows, and infrastructure more secure and robust while explicitly considering usability, the nature of modern scientific collaboration, data sharing, reproducibility, and the use of AI as part of the scientific process.

Applied research proposals should lead to new understandings of scientific infrastructure security properties, secure scientific workflows and benefit domain scientists, transition novel cybersecurity techniques to research cyberinfrastructure, discover vulnerabilities in existing infrastructure, create new pathways for ensuring reproducibility through cybersecurity, or gather meta-data critical to advancing the security of science infrastructure.

The CICI program targets applied security research directly relevant to scientific cyberinfrastructure in support of cross-science discovery, and is intended to complement other OAC programs enabling CI such as Campus Cyberinfrastructure (CC*) and Cyberinfrastructure for Sustained Scientific Innovation (CSSI) . CICI is not the appropriate mechanism for non-cybersecurity infrastructure efforts.

It is also not intended to provide support for fundamental cybersecurity or privacy research; such projects may be better served as submissions to the Secure and Trustworthy Cyberspace (SaTC) program.

CICI comprises four Program Areas outlined below: Usable and Collaborative Security for Science (UCSS) The modern scientific enterprise requires rapid, flexible, and reliable collaboration among participants from varied backgrounds who are using distributed infrastructure and working on problems with a variety of security requirements.

Resource sharing, whether in the form of computation or data, is integral to the modern, CI-intensive scientific process and requires that significant infrastructure exists to facilitate such collaboration. Collaborative scientific experiments may include participants from multiple institutions, laboratories, or organizations physically or logically distributed across campuses, sites, or countries.

Complex technical relationships may exist between users, institutions, and information technology service providers. The security and availability of end-to-end scientific workflows is crucial to the integrity, scalability, speed of discovery, and reproducibility of scientific analyses.

However, domain scientists may lack the knowledge, background, or resources to secure – or understand the security of – research workflows, computation, data, and policies. Ensuring the usability and benefit of cybersecurity for the domain scientists is therefore crucial.

One specific type of proposal in the UCSS area may focus on the initialization of collaborative security operations for research and education activities that support development of secure computing enclaves. Researchers and network operators must work collaboratively to ensure the cyberinfrastructure achieves an appropriate level of both security and usability.

As operational cybersecurity continues to mature, network operators often rely on the concept of a Security Operation Center (SOC). Workshops such as the Workshop on SOC Operations and Construction illustrate advances in both the maturity of SOC operations and the research challenges associated with SOCs.

In the context of research cyberinfrastructure, this could include a multi-organization SOC such as a SOC that creates a regional enclave or SOC that creates an enclave for scientific drivers across many institutions. In other scenarios, a SOC may be associated with a single institution and may even be associated with a single laboratory or experiment.

Correspondingly, the implementation of the SOC may vary from a complex advanced center similar to those found at large companies to a small bespoke collection of tools operated by a single staff or student. Regardless of the scale and configuration, collaboration between researchers and operators is valuable for achieving usable and collaborative security for science.

NSF especially encourages SOC proposals focusing on small and under-resourced institutions that would facilitate the establishment of regional enclaves. In addition to the benefits that can be provided to the underlying CI, NSF views campus and multi-SOC activities as significant opportunities to engage students and train the next generation of security experts.

This program area seeks security and usability research that facilitates scientific collaboration, encourages the adoption of security into the scientific workflow, and/or fosters a holistic, integrated security environment that spans the entire scientific CI ecosystem.

Work in this space should specifically address overcoming security obstacles to data and resource sharing in current science CI and projects, and how to enable domain scientists to more easily and seamlessly integrate security considerations into their scientific workflow.

Such usability-focused efforts are encouraged to take human factors into account and allow scientists to reason over the trade-off between their research goals and security and privacy concerns specific to the research domain. Proposals in this area are strongly encouraged to identify new collaborations, linkages with existing CI, and new functionality that will be enabled by the proposed security or privacy research.

Reference Scientific Security Datasets (RSSD) Scientific cyberinfrastructure, data, and workflows are frequently different from their non-science counterparts, while experiments, collaborations, and analyses may induce different workloads.

For instance, data from a science instrument or sensor may present a unique traffic distribution (e.g., machine-to-machine communication, long-lived or high-volume flows, periodicity), memory access, or authentication patterns. Characterization of normal behavior and usage patterns on cyberinfrastructure can aid in detecting anomalies, including outliers, faults, and attacks.

Further, a better understanding of the characteristic properties of domain or task-specific workloads can help advance the state of the art in testing and evaluation of cybersecurity mechanisms for science CI, engender reproducible security research, and help protect the scientific process.

This area seeks to gather meta-data from operational or otherwise representative CI that can serve as an open community resource for advancing the cybersecurity posture of these systems.

Research of interest in this area includes but is not limited to: instrumenting CI to gather comprehensive and high-fidelity measurements, developing novel methods for collecting, labeling, and curating data from science CI, and methods to share and disseminate security datasets. Efforts toward developing data collection methods and techniques as well as the creation of data artifacts are welcome.

Responsive proposals in this sub-area should consider: Generality and granularity of the data to be collected, its potential value to advancing research in cybersecurity, and potential to protect scientific CI. Examples of specific communities that will benefit from the collected data.

Responsible and ethical data collection and sharing, including protecting any sensitive or personally identifiable information, for instance through anonymization or other means as applicable. The accuracy of any data labels, including anomalous events. The plan to store and share the datasets, including long-term preservation and maintenance.

Metrics for assessing community use and adoption of the datasets.

The intended outcome of an RSSD project is a publicly available dataset that provides the cybersecurity research community a rich source of data to: i) understand operational and/or realistic scientific CI; ii) develop new and novel cybersecurity technologies; and iii) provide realistic data for rigorous and realistic testing, evaluation, and validation of cybersecurity research.

All curated datasets are required to be shared publicly and made available through established community platforms. Awarded projects will work with their program officer to identify the best suited community platform. Proposals are also expected to adhere to FAIR principles .

Costs associated with hosting an object store are permissible in the budget. Transition to Cyberinfrastructure Resilience (TCR) Transitioning cybersecurity research to operational scientific CI can provide benefits to both the CI as well as the target cybersecurity research endeavor itself, and in so doing realize the benefits of translational research.

The primary objective is to improve the security posture of scientific CI by employing the latest cybersecurity innovations. Scientific CI must frequently innovate and evolve to accommodate the challenging requirements of domain scientists, experiments, and collaborations. Further, scientific CI frequently employs unique hardware, software, and configurations data and workloads.

This complexity and traditional lack of security emphasis by domain scientists creates unique and challenging security sensitivities, threats, and constraints for scientific CI. Rather than lag behind on operational cybersecurity practices, scientific CI should instead strive to lead the way in cybersecurity innovation and employ the most promising novel advances in cybersecurity research.

This area therefore welcomes test and evaluation efforts by third- parties, e.g., independent deployment and validation of security and privacy technologies, that result in improved CI security posture.

While the primary objective is to improve the security posture of Scientific CI, the relative openness, flexibility, and agility of research infrastructure presents a potential transition pathway for testing, evaluating, and deploying cybersecurity research.

The unique and often complex ecosystem of software, hardware, configurations, instruments, data, and users in scientific CI can serve to evaluate and validate cybersecurity innovations more comprehensively.

Further, test and evaluation of cybersecurity research within scientific environments can potentially gain insights from real-world conditions, permit causal analysis, and allow for cybersecurity results and experimental data to be shared more broadly within the research community.

Work that transitions either bespoke cybersecurity research innovations tailored for scientific environments or more general cybersecurity research that benefits science CI is welcome. Proposals are encouraged to demonstrate how the approach will directly improve the security posture of scientific CI and have a secondary benefit of demonstrating how cybersecurity innovations can transition into operational practice.

Proposals in this area should seek to improve the robustness of scientific CI through operational or at-scale deployment, test and evaluation of novel cybersecurity research and techniques.

Approaches in this area may include, but are not limited to, applied research in, and transition of: scientific workflow integrity, scientific data sharing, usable security, red-teaming, program analysis, fuzzing, penetration testing, and hardening existing systems and components.

As the scale of datasets used in scientific CI increase and the location of the data becomes more diffused, NSF especially encourages the adoption of information centric approaches that support the use of nearby secure dataset caching rather than having to retrieve data directly from a repository.

The ability to associate integrity and authenticity directly with the data is preferable to approaches the authenticate the data based on its source. Other types of innovative network access techniques might incorporate advances from cellular communication and/or non-terrestrial network communication.

By extending scientific CI to challenging locations, one could potentially increase the ability of researchers to gather data while simultaneously providing emerging network access techniques with an operational user-base to demonstrate the new innovations feasibility. The TCR area further encourages transition activities that advance the deployment and use of reproducibility in CI, workflows, and data.

Reproducibility is core to scientific progress and establishing trust in scientific results. However, mechanisms to support reproducibility are often missing from CI or deployed in an ad- hoc manner.

Proposals focused on transition to support reproducibility are encouraged to consider reproducibility holistically (e.g., inclusive of provenance, integrity, and long-term sustainability) and broadly (e.g., across science domains and infrastructures). Proposals are encouraged to leverage existing research CI, facilities, testbeds, and testing frameworks as applicable.

Proposals must explicitly detail the transition plan; transition platform, pathway, and partners; and quantitative metrics of expected technology maturation or transition success. Integrity, Provenance, and Authenticity for Artificial Intelligence Ready Data (IPAAI) Artificial Intelligence (AI) plays an increasingly important role in scientific CI.

Using AI, researchers can incorporate vast datasets from multiple locations and conduct experiments at a scale that was previously considered infeasible. This creates tremendous new scientific opportunities, but also raises new cybersecurity challenges related to the integrity, provenance, and authenticity of the datasets upon which AI relies.

Unintentional errors in the data used to train AI systems could impact the outcome of experiments across a variety of science drivers. Further, intentional malicious manipulation of datasets could be used to drive AI systems toward invalid and/or misleading results.

The sheer scale of datasets and the ability to ingest datasets from a variety of sources increase the research potential, this same increase also raises potential cybersecurity vulnerabilities. Researchers require tools and techniques to enhance the integrity, provenance, and authenticity of datasets before the data is incorporated into AI models and systems.

Ideally, scientific CI should help ensure the integrity, provenance, and authenticity of datasets, the network connectivity used to provide the data, and the computation systems used in the analysis of the data. The scale and automation enabled by AI can also make these errors difficult and perhaps impossible to detect.

Further, confidence in results and reproducibility can only be achieved if the data used by a model is logged and documented so that the researchers can identify what datasets were used to produce a given result. In the event a dataset is found to be compromised or simply inaccurate, researchers must be able to determine whether that dataset was used in their results.

If a dataset is found be compromised or inaccurate and a researcher is able to determine that AI techniques used that data, it is not clear how the researcher should remove the data from their model. Unlike a static experiment that could simply be repeated without the problematic data, an AI system may have incorporated aspects of the data into its model.

It is anticipated that simply restarting and retraining an AI system from scratch may be infeasible for future large models and systems. The IPAAI area encourages proposals that help ensure the integrity, provenance, and authenticity of dataset and/or communication and/or computation used by scientific AI systems.

By increasing the integrity, provenance, and authenticity of the input to AI systems, the confidence in the resulting output is also increased. For example, an AI system that incorporates data from a scientific instrument might include techniques to ensure the integrity of the data received from the instrument.

AI systems that incorporated medical data might include techniques to determine the data provenance and ensure the dataset does not violate privacy requirements. AI systems that incorporate cached public datasets might include techniques to ensure the cached copy is an authentic version of the original dataset.

In all three of these cases, it is unclear how an AI system might discard the invalid data and recover if say the sensor data was corrupted, medical data violated privacy restrictions, or cached data was (intentionally or unintentionally) modified to remove or replace key features.

Proposals are not limited to these dataset example and these are intended only as examples of why integrity, provenance, and authenticity are critical for AI data. Proposals in this area should seek to improve the integrity, provenance, and authenticity of scientific CI through novel cybersecurity research and techniques. Proposals that help provide verifiable indicators of integrity, provenance, and authenticity are welcome.

Further, inclusion of a comprehensive approach to logging data would be an additional feature, but is not a requirement. The objective for such a feature would be to verify what data was used to produce AI generated results. This allows for reproducibility by other researchers and also allows a researcher to determine if their results relied on data later found to be compromised.

The focus of the IPAAI topic is on integrity, provenance, and authenticity that helps prevent an AI result from incorporating compromised data and optionally provides a verifiable log of what data was used. A reliable log provides both reproducibility by other researchers and for detection if data is determined to be compromised after inclusion.

Any proposal should clearly demonstrate the use of scientific data and corresponding science drivers. Approaches may also benefit other applications, but the primary focus of the proposal should be directed toward the use of AI in science.

CICI program-wide guidelines: All CICI proposals, across all four program areas, must include a description of: Existing scientific infrastructure and distributed scientific environments that will benefit from the proposed research; How the proposed security mechanisms or infrastructure enhancements will advance scientific discoveries, collaborations, and innovations, and benefit scientific applications, users, and communities; Any unique properties of the scientific domain or infrastructure that influence the desired security functionality, design, or mechanisms; The software license that will be used for any released software, and justification for why this license has been chosen; A sustainability plan describing how the proposed system will be supported beyond the project duration; and Any ethical and operational concerns of the work, including obtaining explicit consent of target CI or entities under test, protecting the privacy of sensitive datasets, and establishing processes for informed disclosure as required.

All CICI proposals are encouraged to: Document explicit partnerships or collaborations with one or more domain scientists, research groups, or information technology (IT) support organizations. Partnership documentation from personnel not included in the proposal as PI, co-PI, or senior personnel should be in the form of a letter of collaboration included in the Supplementary Documents section of the proposal.

Explain the threat model upon which the proposed solution is predicated. For reference on a threat model for Open Science, please refer to the Open Science Risk Profile (OSRP) .

Make any software developed under proposed activities publicly available under an open-source license; Provide a plan for gathering quantitative metrics to assess the anticipated security benefits on CI from the proposed work, e.g., science projects or researchers impacted, harms mitigated, etc; and Describe how the proposed work has potential for benefits beyond the lifetime of the award and will benefit groups beyond the proposers themselves.

Anticipated Type of Award: Continuing Grant or Standard Grant Estimated Number of Awards: 12-20 Anticipated Funding Amount: $8,000,000 - $12,000,000 Total funding for the CICI program is $8,000,000 to $12,000,000, subject to the availability of funds.

Each program area will support awards pursuant to the following budget and duration: Usable and Collaborative Security for Science (UCSS) awards will be supported at up to $600,000 total per award for up to 3 years; Reference Scientific Security Datasets (RSSD) awards will be supported at up to $600,000 total per award for up to 3 years; and Transition to Cyberinfrastructure Resilience (TCR) awards will be supported at up to $1,200,000 total per award for up to 3 years; and Integrity, Provenance, and Authenticity for Artificial Intelligence Ready Data (IPAAI)awards will be supported at up to $900,000 total per award for up to 3 years Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

IV. Eligibility Information Who May Submit Proposals: Proposals may only be submitted by the following: Institutions of Higher Education (IHEs): Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the U.S., acting on behalf of their faculty members.

Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of sub-awards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the U.S. campus.

Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. There are no restrictions or limits. Limit on Number of Proposals per Organization: There are no restrictions or limits.

Limit on Number of Proposals per PI or co-PI: An individual can participate as PI, co-PI or senior/key personnel on no more than two CICI proposals. Note that any individual whose biographical sketch is provided as part of the proposal will be considered as Senior/Key Personnel in the proposed activity, irrespective of whether that individual would receive financial support from the project.

In the event that any individual exceeds this limit, any proposal submitted to this solicitation with this individual listed as PI, co-PI, or Senior/Key Personnel after the second