IJRSI Logo

The Future of Zero-Trust Security Architecture with Ai Automation

Authors

Chidiebere Ucheji

Teesside University (United Kingdom)

Article Information

DOI: 10.51244/IJRSI.2026.13010060

Subject Category: Artificial Intelligence

Volume/Issue: 13/1 | Page No: 700-713

Publication Timeline

Submitted: 2026-01-08

Accepted: 2026-01-13

Published: 2026-01-30

Abstract

The rapid growth of cloud computing, remote work, and interconnected digital ecosystems has rendered traditional perimeter-based security models increasingly ineffective. In response, Zero-Trust Security Architecture (ZTSA) has emerged as a dominant cybersecurity paradigm founded on the principle of continuous verification and least-privilege access. However, implementing Zero Trust at scale introduces significant operational complexity due to the need for real-time authentication, contextual risk assessment, and dynamic policy enforcement. This paper examines the future of Zero-Trust security architecture enhanced through artificial intelligence (AI) automation. Drawing on an extensive review of contemporary literature, the study analyses how AI techniques, such as machine learning, behavioural analytics, and automated threat response, can operationalise Zero-Trust principles more effectively. The paper further explores the convergence of AI and Zero Trust, identifying operational benefits alongside technical, ethical, and governance challenges, including data privacy, algorithmic bias, and explainability. To address these issues, a conceptual AI-enabled Zero-Trust automation model is proposed, emphasising continuous learning, adaptive access control, and accountable decision-making. The paper concludes that while AI is critical to the future scalability and effectiveness of ZeroTrust security, its successful deployment depends on robust governance frameworks and sustained human oversight to ensure ethical and trustworthy implementation.

Keywords

Zero-Trust Security Architecture; Artificial Intelligence; Cybersecurity Automation

Downloads

PDF JATS XML

References

1. Aitazaz, F. (2018) Cybersecurity challenges in cloud computing: Ensuring information security for connected devices. ResearchGate. Available at: https://doi.org/10.13140/RG.2.2.19705.99682 [Google Scholar] [Crossref]

2. Ajish, D. (2024) ‘The significance of artificial intelligence in zero trust technologies: a comprehensive review’, Journal of Electrical Systems and Information Technology, 11(1). Available at: https://doi.org/10.1186/s43067-024-00155-z [Google Scholar] [Crossref]

3. Ajoku, C., Ogini, P. and Cookey, I. (2025) ‘The role of artificial intelligence in enhancing threat detection and mitigation in cybersecurity’, International Journal of Economics, Environmental Development and Society, 6(1), pp. 122–131. Available at: https://ijeeds.com.ng/assets/vol.%2C-6(1)-ajoku%2C-c.-m.%2Cogini%2C-p.-b.%2C---cookey%2C-i.-b.pdf (Accessed: 17 December 2025). [Google Scholar] [Crossref]

4. Akbarighatar, P. (2024) ‘Operationalizing responsible AI principles through responsible AI capabilities’, AI and Ethics. Available at: https://doi.org/10.1007/s43681-024-00524-4 [Google Scholar] [Crossref]

5. Akharchaf, Y. (2025) Zero trust architecture in cloud security: Challenges and implementation. ResearchGate. Available at: https://doi.org/10.13140/RG.2.2.26671.04000 [Google Scholar] [Crossref]

6. Ali, Z. and Zafer, B. (2020) Integrating zero-trust architecture with AI-driven threat intelligence in Nigeria’s public sector networks. ResearchGate. Available at: https://doi.org/10.13140/RG.2.2.14869.23526 [Google Scholar] [Crossref]

7. Aramide, O. (2024) ‘Zero-trust identity principles in next-gen networks: AI-driven continuous verification for secure digital ecosystems’, World Journal of Advanced Research and Reviews, 23(3), pp. 3304–3316. Available at: https://doi.org/10.30574/wjarr.2024.23.3.2656 [Google Scholar] [Crossref]

8. Belenguer, L. (2022) ‘AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry’, AI and Ethics, 2(2). Available at: https://doi.org/10.1007/s43681-022-00138-8 [Google Scholar] [Crossref]

9. Blake, H. (2024) Transparency and explainability in AI: Ensuring trust and understanding in autonomous decision-making systems. ResearchGate. Available at: [Google Scholar] [Crossref]

10. https://www.researchgate.net/publication/387419097_Transparency_and_Explainability_in_AI_Ensurin g_Trust_and_Understanding_in_Autonomous_Decision-Making_Systems [Google Scholar] [Crossref]

11. Buczak, A.L. and Guven, E. (2016) ‘A survey of data mining and machine learning methods for cyber security intrusion detection’, IEEE Communications Surveys & Tutorials, 18(2), pp. 1153–1176. Available at: https://doi.org/10.1109/COMST.2015.2494502 [Google Scholar] [Crossref]

12. Budiansyah, A., Zulfan, Z., Nizamuddin, N., Candra, R.A., Ilham, D.N. and Nazaruddin, N. (2025) ‘The effectiveness of machine learning techniques in anomaly detection for cyberattack prevention: systematic literature review 2020–2025’, Brilliance Research of Artificial Intelligence, 5(1), pp. 259–271. Available at: https://doi.org/10.47709/brilliance.v5i1.6124 [Google Scholar] [Crossref]

13. Chinamanagonda, S. (2022) ‘Zero trust security models in cloud infrastructure—adoption of zero-trust principles for enhanced security’, Academia Nexus Journal, 1(2). [Google Scholar] [Crossref]

14. Denzel, N.K. (2025) ‘A survey of security in zero trust network architectures’, GSC Advanced Research and Reviews, 22(2), pp. 182–214. Available at: https://doi.org/10.30574/gscarr.2025.22.2.0036 [Google Scholar] [Crossref]

15. Domalewska, D., Tišma, S. and Veljanovska, K. (2025) ‘Protecting employee privacy and security in the age of digital performance monitoring: a legal comparative analysis of post-communist countries – Poland, Croatia, and the Republic of North Macedonia’, Polish Political Science Yearbook, 54(2), pp. 51–68. Available at: https://doi.org/10.15804/ppsy202518 [Google Scholar] [Crossref]

16. Dommari, S. (2025) ‘AI and behavioral analytics in enhancing insider threat detection and mitigation’, SSRN Electronic Journal. Available at: https://doi.org/10.2139/ssrn.5259337 [Google Scholar] [Crossref]

17. Ejiofor, E., Olusoga, O. and Akinsola, A. (2025) ‘Zero trust architecture: a paradigm shift in network security’, Computer Science & IT Research Journal, 6(3), pp. 104–124. Available at: https://doi.org/10.51594/csitrj.v6i3.1871 [Google Scholar] [Crossref]

18. Faith, B., Simon, B. and Okunola, C.O. (2025) The limitations and biases of AI models in cybersecurity decision-making. ResearchGate. Available at: [Google Scholar] [Crossref]

19. https://www.researchgate.net/publication/396865236_The_Limitations_and_Biases_of_AI_Models_in_ Cybersecurity_Decision-_Making (Accessed: 17 December 2025). [Google Scholar] [Crossref]

20. Ghasemshirazi, S., Shirvani, G. and Alipour, M.A. (2023) Zero trust: applications, challenges, and opportunities. arXiv preprint arXiv:2309.03582. Available at: https://arxiv.org/abs/2309.03582 [Google Scholar] [Crossref]

21. Gilbert, C., Gilbert, M.A. and Dorgbefu, M. (2025) ‘Detection and response strategies for advanced persistent threats (APTs)’, International Journal of Scientific Research and Modern Technology, 4(4), pp. 5–21. Available at: https://doi.org/10.38124/ijsrmt.v4i4.367 [Google Scholar] [Crossref]

22. Gilbert, C., Gilbert, M.A. and Jnr, M.D. (2025) ‘Secure data management in cloud environments’, International Journal of Research and Innovation in Applied Science, 9(4), pp. 25–56. Available at: https://doi.org/10.51584/IJRIAS.2025.10040003 [Google Scholar] [Crossref]

23. Gilbert, C., Gilbert, M.A., Dorgbefu, M., Leakpor, D.J., Gaylah, K.D. and Adetunde, I.A. (2025) ‘Enhancing detection and response using artificial intelligence in cybersecurity’, International Journal of Multidisciplinary Research and Publications (IJMRAP), 7(10), pp. 87–104. [Google Scholar] [Crossref]

24. Grant, A.R. and Reynolds, M.T. (2025) Adaptive zero-trust access control using AI-based behavioral analytics in hybrid clouds. ResearchGate. Available at: https://www.researchgate.net/publication/398638718_Adaptive_Zero-Trust_Access_Control_Using_AIBased_Behavioral_Analytics_in_Hybrid_Clouds [Google Scholar] [Crossref]

25. Gurram, A. (2025) ‘Generative AI for enhanced cybersecurity: building a zero-trust architecture with agentic AI’, World Journal of Advanced Engineering Technology and Sciences, 15(1), pp. 2380–2396. Available at: https://doi.org/10.30574/wjaets.2025.15.1.0504 [Google Scholar] [Crossref]

26. Haider, M. and Bhutto, B. (2022) Reinforcing cybersecurity with zero trust and AI-powered strategies. [Google Scholar] [Crossref]

27. He, Y., Huang, D., Chen, L., Ni, Y. and Ma, X. (2022) ‘A survey on zero trust architecture: challenges and future trends’, Wireless Communications and Mobile Computing, 2022(1), Article ID 6476274. [Google Scholar] [Crossref]

28. Hohmann, B. and Kollár, G. (2025) ‘Reflections on the data protection compliance of AI systems under the EU AI Act’, Cogent Social Sciences, 11(1). Available at: https://doi.org/10.1080/23311886.2025.2560654 [Google Scholar] [Crossref]

29. Hsia, J. (2025) AI-powered risk assessment in zero trust security. SSRN. Available at: https://doi.org/10.2139/ssrn.5146370 [Google Scholar] [Crossref]

30. Hussain, S. (2025) Behavioral biometrics and continuous authentication in cybersecurity systems. ResearchGate. Available at: https://doi.org/10.13140/RG.2.2.35971.41763 [Google Scholar] [Crossref]

31. Ignacio, S., Alejandra, P., Ivan, D. and Allende, H. (2025) ‘Zero-day threat mitigation via deep learning in cloud environments’, Applied Sciences, 15(14), p. 7885. Available at: https://doi.org/10.3390/app15147885 [Google Scholar] [Crossref]

32. James, U.U., Idika, C.N. and Enyejo, L.A. (2023) ‘Zero trust architecture leveraging AI-driven behavior analytics for industrial control systems in energy distribution networks’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 685–709. Available at: https://doi.org/10.32628/cseit23564522 [Google Scholar] [Crossref]

33. Jena, K. (2023) ‘Zero-trust security models overview’, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, p. 578. Available at: https://doi.org/10.32628/cseit2390 [Google Scholar] [Crossref]

34. Johnson, A.D., Simmons, R.L., Carter, E.M., Brooks, N.J. and James, C. (2025) ‘Deep learning for intrusion detection in zero trust frameworks’. [Google Scholar] [Crossref]

35. Kang, H., Liu, G., Wang, Q., Meng, L. and Liu, J. (2023) ‘Theory and application of zero trust security: a brief survey’, Entropy, 25(12), p. 1595. [Google Scholar] [Crossref]

36. Kindervag, J. (2010) No more chewy centers: introducing the zero trust model of information security. Forrester Research, 3(1), pp. 1–16. [Google Scholar] [Crossref]

37. Kisina, D., Oyinomomo-Emi, E., Akpe, Owoade, S., Ubanadu, B., Gbenle, T., Oluwasanmi, S. and Adanigbo (2022) A conceptual framework for implementing zero trust principles in cloud and hybrid IT environments. Available at: https://www.irejournals.com/formatedpaper/1708124.pdf (Accessed: 16 December 2025). [Google Scholar] [Crossref]

38. Kothamali, P. and Banik, S. (2022) Limitations of signature-based threat detection. ResearchGate. Available at: https://www.researchgate.net/publication/388494583_Limitations_of_SignatureBased_Threat_Detection [Google Scholar] [Crossref]

39. Lund, B.D., Lee, T.-H., Wang, Z., Wang, T. and Mannuru, N.R. (2024) ‘Zero trust cybersecurity: procedures and considerations in context’, Encyclopedia, 4(4), pp. 1520–1533. Available at: https://doi.org/10.3390/encyclopedia4040099 [Google Scholar] [Crossref]

40. Mangla, M. (2025) ‘Behavioral analytics and AI in zero trust security: a framework for adaptive identity and access management’, International Journal of Science and Technology, 4(1), pp. 54–75. Available at: https://doi.org/10.56127/ijst.v4i1.2275 [Google Scholar] [Crossref]

41. Mangla, M. and Kumar, D. (2023) ‘AI-driven zero trust architecture: a scalable framework for threat detection and adaptive access control’, International Journal of Science and Technology, 2(3), pp. 117– 124. Available at: https://doi.org/10.56127/ijst.v2i3.22 [Google Scholar] [Crossref]

42. Mccall, A. (2024) Cybersecurity in the age of AI and IoT: emerging threats and defense strategies. ResearchGate. Available at: https://www.researchgate.net/publication/386050391_Cybersecurity_in_the_Age_of_AI_and_IoT_Emer ging_Threats_and_Defense_Strategies [Google Scholar] [Crossref]

43. Meng, L., Huang, D., An, J., Zhou, X. and Lin, F. (2022) ‘A continuous authentication protocol without trust authority for zero trust architecture’, China Communications, pp. 198–213. Available at: https://doi.org/10.23919/jcc.2022.08.015 [Google Scholar] [Crossref]

44. Mensah, F. (2024) ‘Zero trust architecture: a comprehensive review of principles, implementation strategies, and future directions in enterprise cybersecurity’, International Journal of Academic and [Google Scholar] [Crossref]

45. Industrial Research Innovations (IJAIRI), 10(6), pp. 339–346. Available at: https://www.researchgate.net/publication/391428379_Zero_Trust_Architecture_A_Comprehensive_Revi ew_of_Principles_Implementation_Strategies_and_Future_Directions_in_Enterprise_Cybersecurity [Google Scholar] [Crossref]

46. Mitchell, J.R., Brooks, O.N., Simmons, D.K. and James, C. (2025) Reinforcement learning for automated policy enforcement in zero trust networks. ResearchGate. Available at: [Google Scholar] [Crossref]

47. https://www.researchgate.net/publication/396744657_Reinforcement_Learning_for_Automated_Policy_ Enforcement_in_Zero_Trust_Networks (Accessed: 17 December 2025). [Google Scholar] [Crossref]

48. Mudau, K., Mudumani, K. and Zwane, S.M. (2025) Zero trust architecture: frameworks and implementation strategies in modern cybersecurity. Zenodo. Available at: https://doi.org/10.5281/zenodo.17404160 [Google Scholar] [Crossref]

49. Muhtukkumaran, B. (2025) ‘Artificial intelligence in cybersecurity threat detection: methods, challenges, and future directions’, International Journal of Recent Trends in Innovation (IJRTI), 10, p. 72. Available at: https://www.ijrti.org/papers/IJRTI2508013.pdf (Accessed: 17 December 2025). [Google Scholar] [Crossref]

50. Mushtaq, S., Mohsin, M. and Mushtaq, M.M. (2025) ‘A systematic literature review on the implementation and challenges of zero trust architecture across domains’, Sensors, 25(19), p. 6118. Available at: https://doi.org/10.3390/s25196118 [Google Scholar] [Crossref]

51. Nnenna, J., Adeniji, S.A., Onwuegbuchi, N.O. and Sanni, N.S. (2025) ‘Analyzing the use of machine learning techniques in detecting fraudulent activities’, World Journal of Advanced Research and Reviews, 26(1), pp. 1198–1209. Available at: https://doi.org/10.30574/wjarr.2025.26.1.1097 [Google Scholar] [Crossref]

52. Nzeako, G. and Shittu, R. (2024) ‘Implementing zero trust security models in cloud computing environments’, World Journal of Advanced Research and Reviews, 24(3), pp. 1647–1660. Available at: https://doi.org/10.30574/wjarr.2024.24.3.3500 [Google Scholar] [Crossref]

53. Obuse, E., Ayanbode, N., Cadet, E., Etim, E.D. and Essien, I.A. (2022) ‘Natural language processing for cybersecurity: automating threat report analysis’, International Journal of Multidisciplinary Research and Growth Evaluation, 3(4), pp. 708–723. Available at: https://doi.org/10.54660/.ijmrge.2022.3.4.708-723 [Google Scholar] [Crossref]

54. Omopariola, M.K. (2016) Zero-trust architecture deployment in emerging economies: a case study from Nigeria. ResearchGate. Available at: https://doi.org/10.13140/RG.2.2.23970.54727 [Google Scholar] [Crossref]

55. Paradhi, D., Ansari, M.N. and More, S. (2024) ‘Anomaly detection in network traffic using unsupervised machine learning’, International Journal of Advanced Research in Science, Communication and Technology, pp. 418–425. Available at: https://doi.org/10.48175/ijarsct-19264 [Google Scholar] [Crossref]

56. Patil, D., Rane, N.L., Desai, P. and Rane, J. (2024) ‘Machine learning and deep learning: methods, techniques, applications, challenges, and future research opportunities’, Trustworthy Artificial Intelligence in Industry and Society. Available at: https://doi.org/10.70593/978-81-981367-4-9_2 [Google Scholar] [Crossref]

57. Pigola, A. and Fernando, M. (2025) ‘Zero trust in cybersecurity: managing critical challenges for effective implementation’, Journal of Systems and Information Technology. Available at: https://doi.org/10.1108/jsit-08-2024-0326 [Google Scholar] [Crossref]

58. Pigola, A., de Souza Meirelles, F. and da Costa, P.R. (2025) ‘Trust Management in the Age of Zero trust: a comprehensive multi-method analysis from enterprise challenges’, Enterprise Information Systems. doi: 10.1080/17517575.2025.2588753. [Google Scholar] [Crossref]

59. Popoola, S. (2025) Ethical and Regulatory Challenges of AI-Driven Decision-Making in Financial Services. ResearchGate. Available at: https://www.researchgate.net/publication/396689503_Ethical_and_Regulatory_Challenges_of_AIDriven_Decision-_Making_in_Financial_Services (Accessed: 17 December 2025). [Google Scholar] [Crossref]

60. Poudel, R., Rahman, M.M., Rahman, M.M., Rahman, M.M. and Dhakal, K. (2023) ‘Adversarial Attacks on AI Systems: A Growing Cyber Threat’, International Journal of Science and Research Archive, 10(2), pp. 1438–1450. doi: 10.30574/ijsra.2023.10.2.1086. [Google Scholar] [Crossref]

61. Rony, M.A. (2025) ‘AI-Enabled Predictive Analytics and Fault Detection Frameworks for Industrial Equipment Reliability and Resilience’, ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), pp. 705–736. [Google Scholar] [Crossref]

62. Saeed, S., Altamimi, S.A., Alkayyal, N.A., Alshehri, E. and Alabbad, D.A. (2023) ‘Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations’, Sensors, 23(15). doi: 10.3390/s23156666. [Google Scholar] [Crossref]

63. Sarrat, T. and Finn, T. (2025) Surveillance Psychology: Ethical Risks of Continuous Behavioral Monitoring with AI in Schools and Workplaces. Available at: https://www.researchgate.net/publication/391635518_SURVEILLANCE_PSYCHOLOGY_ETHICAL RISKS_OF_CONTINUOUS_BEHAVIORAL_MONITORING_WITH_AI_IN_SCHOOLS_AND_WORKPLACES (Accessed: 17 December 2025). [Google Scholar] [Crossref]

64. Singh, H. (2017) ‘Key Cloud Security Challenges for Organizations Embracing Digital Transformation Initiatives’, 3(6), pp. 19–25. Available at: https://www.researchgate.net/publication/394465299_Key_Cloud_Security_Challenges_for_Organizatio ns_Embracing_Digital_Transformation_Initiatives (Accessed: 17 December 2025). [Google Scholar] [Crossref]

65. Sophia, E. (2025) AI-Driven Behavioral Biometrics for Continuous Authentication in Zero Trust. Available at: https://www.researchgate.net/publication/396657266_AI-DRIVEN_BEHAVIORAL_ANALYTICS_FOR_CONTINUOUS_AUTHENTICATION_IN_ZERO_TR UST_SECURITY_MODELS (Accessed: 17 December 2025). [Google Scholar] [Crossref]

66. Sultana, H. (2024) ‘Machine Learning for Cybersecurity: Threat Detection and Prevention’, ShodhKosh: Journal of Visual and Performing Arts, 5(7). doi: 10.29121/shodhkosh.v5.i7.2024.4592. [Google Scholar] [Crossref]

67. Sunkara, G. (2025) ‘Implementing Zero Trust Architecture in Modern Enterprise Networks’, SAMRIDDHI: A Journal of Physical Sciences Engineering and Technology, 17(03), pp. 1–11. doi: 10.18090/samriddhi.v17i03.01. [Google Scholar] [Crossref]

68. Syed, N.F., Shah, S.W., Shaghaghi, A., Anwar, A., Baig, Z. and Doss, R. (2022) ‘Zero trust architecture (ZTA): A comprehensive survey’, IEEE Access, 10, pp. 57143–57179. [Google Scholar] [Crossref]

69. Talan, A. (2022) Zero trust network access with cybersecurity challenges and potential solutions (Doctoral dissertation, Dublin, National College of Ireland). [Google Scholar] [Crossref]

70. Turgunov, J.S., Rakhimova, M.O., Saidov, K.B. and Dharmasena, S. (2025) Human-in-the-loop systems for ethical AI. ResearchGate. Available at: https://www.researchgate.net/publication/393802734_HUMAN-IN-THE-LOOP_SYSTEMS_FOR_ETHICAL_AI (Accessed: 17 December 2025). [Google Scholar] [Crossref]

71. Vaka, P.R. (2021) ‘Zero Trust Security Model’, International Journal of Advanced Research in Engineering & Technology, 12(6), pp. 148–156. Available at: https://www.researchgate.net/publication/387437826_Zero_Trust_Security_Model (Accessed: 17 December 2025). [Google Scholar] [Crossref]

72. Villegas, W.E., Gutierrez, R., Navarro, A.M. and Mera-Navarrete, A. (2025) ‘Adaptive Authentication and Access Control System in Dynamic Educational Environments Based on AI’, Computer, 58(8), pp. 53–63. doi: 10.1109/mc.2025.3565149. [Google Scholar] [Crossref]

73. Vitla, S. (2023) ‘User Behavior Analytics and Mitigation Strategies through Identity and Access Management Solutions: Enhancing Cybersecurity with Machine Learning and Emerging Technologies’, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(03). doi: 10.61841/turcomat.v14i03.14967. [Google Scholar] [Crossref]

74. Wells, A., Ajeigbe, K.J. and Stern, M. (2020) Security Trends in Networking: From Traditional Approaches to Zero Trust Architectures. ResearchGate. Available at: https://www.researchgate.net/publication/389874910_Security_Trends_in_Networking_From_Tradition al_Approaches_to_Zero_Trust_Architectures (Accessed: 16 December 2025). [Google Scholar] [Crossref]

75. Wylde, A. (2021) ‘Zero trust: Never trust, always verify’, 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). doi: 10.1109/cybersa52016.2021.9478244. [Google Scholar] [Crossref]

76. Yalla, M.R. (2024) ‘Zero-trust security architecture in the AI era: a novel framework for enterprise cyber resilience’, International Journal of Science and Research Archive, 13(2), pp. 4341–4356. doi: 10.30574/ijsra.2024.13.2.0172. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles