Volume no :
|Issue no :
Article Type :
Author :
Mr.Sidharth SharmaPublished Date :
Publisher :
Page No: 1 - 7
Abstract : The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and micro-segmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape.
Keyword: Zero Trust Architecture, Cybersecurity Framework, Identity Verification, Least Privilege Access, AI in Security, Threat Detection, Cloud Security, Micro-Segmentation, Zero Trust Networks, Risk Mitigation.
Reference:
1. Hunt, E. B. (2014). Artificial intelligence. Academic Press.
2. Holmes, J., Sacchi, L., &Bellazzi, R. (2004). Artificial intelligence in medicine. Ann R Coll Surg Engl, 86, 334-8.
3. Winston, P. H. (1992). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
4. Winston, P. H. (1984). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
5. Boden, M. A. (Ed.). (1996). Artificial intelligence. Elsevier.
6. Thepade, D. S., Mandal, P. R., & Jadhav, S. (2015). Performance Comparison of Novel Iris Recognition Techniques Using Partial Energies of Transformed Iris Images and EnegyCompactionWith Hybrid Wavelet Transforms. In Annual IEEE India Conference (INDICON).