Data-Driven Excellence: Navigating the Future of Retail Cybersecurity with Machine Learning, Business Analytics, and Blockchain Applications


  • Benjamin Lee, Bo James Department of Computer science, University of Canada


Retail Cybersecurity, Machine Learning, Business Analytics, Blockchain, Data-driven Strategies, Threat Mitigation, Customer Trust, Digital Marketplace


This article explores the paradigm shift in retail cybersecurity through the lens of data-driven strategies, integrating machine learning, business analytics, and blockchain applications. The escalating threat landscape in the digital retail space necessitates innovative approaches to fortify defenses. The methodology section outlines a comprehensive approach, encompassing data collection, machine learning model development, business analytics integration, and blockchain implementation. Results demonstrate enhanced threat detection, transaction security, and overall resilience. The discussion underscores the synergy of technologies, adaptive threat response, and improved customer trust. Challenges such as data privacy, integration complexities, workforce skills, costs, and resistance to change are identified, and treatments are proposed. These treatments advocate for data privacy governance, streamlined integration, workforce development, cost-effective strategies, and effective change management. The conclusion emphasizes the transformative potential of the integrated approach, contributing to the ongoing discourse on securing retail operations amidst dynamic cyber threats. The findings underscore the importance of data-driven excellence in navigating the complexities of retail cybersecurity, ensuring a secure and resilient future for the industry.