Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks
Blessing Austin-Gabriel, Nurudeen Yemi Hussain, Adebimpe Bolatito Ige, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, Adeoye Idowu Afolabi · Open Access Research Journal of Engineering and Technology · 2021
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.