Enhanced Security Encryption and Data Driven Model for Digital transition using Artificial Intelligence - Nguyen Van Thanh, Dang Thanh Le (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In an era of digital data security, ensuring robust security measures is paramount to safeguarding sensitive data. This paper proposes an innovative approach combining enhanced security encryption and a data-driven model empowered by artificial intelligence (AI) to fortify digital transitions. The methodology begins with a meticulous assessment of objectives, data inventory, and classification to discern the scope and sensitivity of information involved. An encryption strategy tailored to the data’s sensitivity level is then devised, encompassing encryption at rest, in transit, and end-to-end encryption where applicable. Integrating AI into the security framework enables real-time threat detection through sophisticated algorithms analyzing network traffic, user behavior, and system logs. Moreover, AI-driven behavioral analytics augment monitoring capabilities, enabling the identification of anomalies indicative of potential security breaches. By amalgamating encryption with AI-driven insights, this approach presents a comprehensive solution to fortify digital transitions, ensuring data integrity, confidentiality, and compliance with regulatory standards.