Improving REST API Security and Software Integrity through Automated PII Detection Tool Using Machine Learning Techniques

The increasing reliance on REST APIs for data transmission has heightened the risk of exposing Personally Identifiable Information (PII). This underscores the need for effective detection systems to safeguard privacy and comply with regulations such as GDPR and Indonesia's Personal Data Protection Acts. This study introduces a machine learning-driven Personally Identifiable Information (PII) identification system utilizing Support Vector Machines (SVM) to examine semi-structured API responses. The system consists of two primary components: a backend module that executes PII detection and archives results in a database, and an interactive dashboard that allows users to examine, oversee, and administer detection results. The backend exhibits robust performance with elevated accuracy and precision, whereas the dashboard provides functionalities including search, severity filtering, and comprehensive result display to improve user engagement and operational decision-making. Collectively, these elements offer a comprehensive solution for enhancing REST API security and ensuring software processes comply with data privacy standards. Future endeavors will investigate real-time warning systems, sophisticated data visualization, and extensive domain modification to augment the system's efficacy.