Regulation Relatedness Map Creation Method with Latent Semantic Analysis

Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves by the help of this relation maps. These maps are created generally by an expert judgment or top-down relationship analysis. These methods are subjective and easily creates inconsistencies. In order to have an objective and statistical relationships map, we propose a Latent Semantic Analysis (LSA) based modal to generate a specific relatedness correlation map. We created a relatedness map of a banking regulation to a best practice. We analyzed 224 statements of this regulation in relation to Control Objectives for Information Technologies (Cobit) 2019's 1202 activities. Furthermore, we support our LSA results with MCDM analysis methods; Fuzzy Analytics Hierarchy Process (FAHP) to prioritize our criteria and, WASPAS (Weighted Aggregated Sum Product Assessment Method) to compare similarity results of regulation and Cobit activity pairs. Instead of the subjective methods for mapping best practices and regulations, this study suggests creating relatedness maps supported by the objectivity of LSA.