Cyber Risk Assessment and Mitigation Strategies for Ensuring Data Protection in Cross-Border E-Commerce Operations

Abstract

The swift growth of cross-border e-commerce (CBEC) has made companies more vulnerable to cyber threats, such as fraud, data breaches, and system flaws. Ensuring strong data security is critical for maintaining consumer trust and regulatory compliance. Machine learning (ML) and deep learning (DL) can help detect patterns and predict cyber threats, enhancing threat detection capabilities. However, cross-border enterprises must navigate differing cybersecurity regulations between countries, complicating risk management strategies. The goal is to evaluate the cyber risks associated with CBEC activities and advance appropriate mitigation techniques to improve data security in the network. It emphasizes the importance of legal compliance, encryption techniques, and multi-factor authentication in minimizing risks. It also highlights the issues such as the lack of uniform cybersecurity rules, the difficulty of managing multinational data storage, and the changing nature of cyber-attacks. It shows that firms with comprehensive, dynamic risk management systems have a greater chance of mitigating potential attacks. Finally, a proactive, multi-layered cybersecurity policy is required to protect consumer data in the network and ensure the reliability of CBEC operations.