Zero Trust Security: Redefining Cyber Defense Strategies
Keywords:
Zero Trust Security, Ransomware, CCPA, HIPAA, GDPR, Artificial Intelligence, Access controlAbstract
Zero Trust Security (ZTS) is a relatively new approach to the cybersecurity industry since relying solely on the perimeter protection model has proven ineffective against today’s threats. Contrary to other models that presuppose a network boundary can be protected, ZTS presumes that all access requests can be intrusive. This model has gained popularity because more and more enterprises embrace cloud technologies, decentralized networks, and distributed work arrangements. It also includes features such as continuous authentication, micro-segmentation, least privilege access against APTs, insider threats, and supply chain threats at ZTS. Thus, in the times of ransomware attacks and data breaches, ZTS is a good, agiler planning strategy in cybersecurity. What makes it more relevant to this case is that its implementation matches current legal policies like GDPR and HIPAA better than other frameworks, which would make protecting important data impossible. This article aims to explain what would be considered a basic theoretical framework theory and the challenges in its implementation within the practical setting using ZTS as an example. You want to establish how such a theory impacts the existing cybersecurity paradigms.
References
• Calder, A. (2018). NIST Cybersecurity Framework: A Pocket Guide. IT Governance Ltd.
• Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems, 6(4), Article 13. https://dl.acm.org/doi/10.1145/2843948
• Kindervag, J. (2010). No More Chewy Centers: Introducing the Zero Trust Model of Information Security. Forrester Research. https://www.forrester.com/report/No-More-Chewy-Centers/RES56682
• Liska, A. (2016). Ransomware: Defending Against Digital Extortion. Syngress.
• Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207
• Schneier, B. (2015). Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. W.W. Norton & Company. https://www.schneier.com/books/data-and-goliath/
• Scott, R., & Simon, E. (2018). The Evolution of Zero Trust. SANS Institute Whitepaper. https://www.sans.org/white-papers/39510/
• Shackleford, D. (2016). Cloud Security Fundamentals. Syngress.
• Spies, M. (2017). Cybersecurity and Data Privacy: An International Perspective. Springer.
• Rahaman, M. M., Rani, S., Islam, M. R., & Bhuiyan, M. M. R. (2023). Machine Learning in Business Analytics: Advancing Statistical Methods for Data-Driven Innovation. Journal of Computer Science and Technology Studies, 5(3), 104-111.
• Linkon, A. A., Noman, I. R., Islam, M. R., Bortty, J. C., Bishnu, K. K., Islam, A., ... & Abdullah, M. (2024). Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Melitus. IEEE Access.
• Sumon, M. F. I., Khan, M. A., & Rahman, A. (2023). Machine Learning for Real-Time Disaster Response and Recovery in the US. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 700-723.
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