Investigation of Threats to Data States in Data Loss Prevention Using the Concept of Complex Linear Diophantine Fuzzy Relations

In today's digital age, many operational decisions in businesses rely on data sources, enabling organizations to enhance productivity, make informed decisions, and gain competitive advantages. However, businesses also face data breaches involving sensitive information—such as financial records, intellectual property, and customer personal data—which may be compromised inadvertently. These threats can often be mitigated by implementing robust cybersecurity measures, such as Data Loss Prevention (DLP), to ensure proper monitoring and control of all organizational data, enforce policies without exceptions, and prevent unauthorized data transfers or rule violations. Despite these measures, uncertainties remain regarding the efficacy of identifying threats at various stages of data loss to mitigate their adverse effects through effective cybersecurity. To address this, this paper introduces Complex Linear Diophantine Fuzzy Relations (CLDFRs). For the first time in fuzzy set theory, we analysed the relationships between various threats and components of DLP-based data loss solutions. Additionally, we present the concept of Hasse diagrams for Complex Linear Diophantine Fuzzy Sets and Relations to examine different cybersecurity methods and procedures. This approach helps determine the most effective strategy based on Hasse diagram analysis. Furthermore, after applying specific constraints to the decision-making process, the optimal cybersecurity approach is selected. Finally, a comparative analysis demonstrates the advantages of the proposed methods.