# Cybersecurity in a Large-Scale Research Facility--One Institution's Approach
## 4 FAIR and Open Science at the NHMFL
### Challenges of Data Security for FAIR and Open Data  
The risks of data breach and theft are inherent to scientific research when using a combination of vintage and modern RT and CI and not unique to an environment where FAIR and open-science principles are emphasized. However, the application of these principles creates novel challenges for data security and integrity for researchers, data standards, cybersecurity practitioners, and IT professionals. For example, visions for the future of FAIR data within many scientific disciplines universally require interconnected, interoperable, global CI and data ecosystems to facilitate the desired functionality [47, 48]. This includes the ability for researchers to use high-performance computing to carry out complex analyses on large volumes of data from federated data repositories around the globe with controlled access to sensitive data, such as protected health information or other regulated data. If not properly managed, the complexity of the CI needed to facilitate this functionality can create additional information privacy and cybersecurity risk by opening more avenues of attack, as a single insecure node in a larger system could potentially compromise the security of the entire network. Although not new, the concept of a Science DMZ network [49] has been proposed to address some of the concerns related to maintaining network security while facilitating high-speed access to data. The Science DMZ offers one viable path forward for institutions to enhance the cybersecurity of their FAIR-related CI. However, network security is only one aspect of successful implementation. Other aspects of the solution must address data access and integrity (data management/stewardship) requirements. Furthermore, FAIR data ecosystems need to be broadly utilized, be inclusive, and have global reach to ensure their long-term sustainability. There is no guarantee that all institutions contributing to a particular FAIR data ecosystem will have the resources or expertise needed to manage or implement all necessary parts of the required cybersecurity and data management solutions; i.e., the Science DMZ, data repositories, and related access and data management workflows. Another major challenge to making data from scientific research FAIR is that the availability of metadata, workflows, and other components of the overall data product enables them to be reused more effectively for both legitimate and malicious reasons. The rich metadata and interoperable nature of FAIR data could allow malicious actors to more easily recognize the value of stolen data and utilize it, potentially increasing the motivation for cyberattackers to attempt a data breach. To illustrate this point, we can utilize the FAIR data maturity model developed by a Research Data Alliance working group [50, 51] to show scenarios where research data are not FAIR and others that are very FAIR. The FAIR data maturity model provides indicators that can be used to judge the \"FAIRness\" of research data. Among these indicators are that data and metadata are expressed in standardized formats (RDA-II-01), that rich metadata are provided to allow discovery (RDA-F2-01M), that metadata can be accessed manually (RDA-A1-02M), and that a plurality of relevant and accurate attributes are provided to allow reuse (RDA-R1-01M). Using these indicators, we can predict that, in practice, \"not FAIR\" data might be stored in proprietary file formats which require specific, expensive software or non-standard hardware or research processes to utilize. Data could also be stored in file formats that are trivial to open but without necessary metadata that make it possible to understand, such as a columnar text file with generic column names such as \"Coll\", \"Col2\", etc. There would also be no human-readable text, such as a \"README\" file, to provide context or meaning to any of the associated data files. Conditions, such as these, do not facilitate data reuse [52].