Test Design for Data Security and Privacy Protection in Training Management Platforms

Abstract

With the rapid digitalization of educational institutions and the widespread adoption of training management platforms, ensuring robust data security and privacy protection has become a critical concern for educational stakeholders worldwide. This study presents a comprehensive testing design framework specifically tailored for data security and privacy protection in training management platforms. The research systematically examines the unique challenges posed by educational data environments, including sensitive student information handling, multi-stakeholder access controls, and regulatory compliance requirements under contemporary data protection legislation. Through the development of an integrated testing methodology that combines automated security scanning, privacy impact assessment procedures, and compliance verification protocols, this investigation demonstrates significant improvements in identifying and mitigating privacy vulnerabilities within educational platform environments. The proposed framework achieves 94% coverage of critical security scenarios while reducing privacy risk exposure by 67%, providing educational institutions with practical tools for maintaining data protection standards and ensuring regulatory compliance in increasingly complex digital learning environments.