Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks
1 Babcock University, Ilishan-Remo, Ogun State, Nigeria.
2 M&M Technical Services, Nigeria.
3 Independent Researcher, Canada.
4 Independent Researcher, Lagos Nigeria.
5 Amstek Nigeria Limited, olukunle.
6 Independent Researcher, Nigeria.
Review
Open Access Research Journal of Engineering and Technology, 2021, 01(01), 047-055.
Article DOI: 10.53022/oarjet.2021.1.1.0107
Publication history:
Received on 19 February 2021; revised on 18 March 2021; accepted on 21 March 2021
Abstract:
This paper explores the integration of Artificial Intelligence (AI) and data science into Zero Trust Architecture (ZTA) to enhance enterprise cybersecurity frameworks. Zero Trust Architecture fundamentally shifts away from traditional perimeter-based security models, adopting a "never trust, always verify" approach to ensure robust protection against sophisticated cyber threats. AI and data science significantly bolster ZTA by enabling advanced threat detection, predictive analytics, and continuous monitoring. The paper outlines a comprehensive framework for integrating AI with ZTA, detailing the critical technologies and tools required for successful implementation. It also discusses best practices for deployment and identifies potential pitfalls and mitigation strategies. Key findings highlight the transformative potential of AI-enhanced ZTA in providing dynamic, scalable, and effective security solutions. For enterprises considering this approach, recommendations are provided, emphasizing the importance of strategic planning, comprehensive training, and regular audits. Future research directions are suggested to further advance the field, focusing on developing more sophisticated AI algorithms, integrating emerging technologies, privacy-preserving techniques, scalability, and human-AI collaboration.
Keywords:
Zero Trust Architecture; Artificial Intelligence (AI); Cybersecurity; Predictive Analytics; Threat Detection; Data Science
Full text article in PDF:
Copyright information:
Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0