Engine for Discovery of Personal Data Based on Machine Learning

Although numerous privacy laws existed prior to its enactment, the GDPR has brought privacy issues to the forefront of regulatory attention. Organizations must ensure that their personal data management processes, particularly those involving the collection, storage, and processing of data within IT systems, comply with regulatory requirements. Personal data discovery in IT systems refers to the process of identifying and cataloguing personal data across an organization's digital infrastructure. It involves scanning databases, file systems, and applications to locate sensitive information such as names, addresses, or financial details. By automating this process, organizations can better understand their data landscape, manage risks, and ensure proper handling of Personal identifiable information (PII). Personal data discovery is a critical first step for any later activity related to the personal data in IT systems in organizations. This paper describes the design of the Personal Data Discovery Engine (PDDE), process flows and how main modules and their functionalities were implemented. A Pseudo algorithm based on Machine Learning (ML) for discovery and classification of personal data from heterogenous text and tabular datasets found in IT Systems is defined.