Authors :
Kishan Raj Bellala
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/4xnuh3xu
Scribd :
https://tinyurl.com/5n7ex4hk
DOI :
https://doi.org/10.38124/ijisrt/25apr1143
Google Scholar
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Abstract :
Enterprises face a critical security challenge when they deploy hybrid cloud systems because these systems
combine public cloud scalability with private cloud data control. Zero-trust security frameworks must be adopted because
traditional perimeter-based security methods no longer work in hybrid cloud environments with their dynamic and
decentralized nature. Every person’s system and device must prove their identity under the zero-trust model because no
entity should receive unconditional trust regardless of its location. The hybrid cloud environment demands advanced
security approaches because it handles massive amounts of data while facing complex modern cyber threats. This paper
investigates the implementation of artificial intelligence (AI) systems to boost zero-trust security protection within hybrid
cloud infrastructure. The research investigates present trends and upcoming directions to develop an extensive framework
which uses artificial intelligence for zero-trust security protection of hybrid cloud systems against modern cyber threats.
We analyze the advantages and obstacles and ethical aspects of implementing zero trust for AI together with its actual
usage in hybrid cloud systems. The research provides a complete method to use artificial intelligence for improving Zero
Trust security in hybrid cloud systems through analysis of present trends and future development possibilities. Such
measures will establish an active intelligent and strong defense mechanism against present and future cyber threats.
Keywords :
Hybrid Cloud, Public Cloud, Private Cloud, Zero Trust, Zero Trust Security Framework, Perimeter-Based Security, Decentralized Security, Cyber Threats, Artificial Intelligence (AI), Scalability, Flexibility, Resilient Defense, Security Challenges, Ethical Considerations, Future Developments.
References :
- Parisa, S. K., Banerjee, S., & Whig, P. (2023). AI-Driven Zero Trust Security Models for Retail Cloud Infrastructure: A Next-Generation Approach. International Journal of Sustainable Development in the field of IT, 15(15).
- Horne, D., & Nair, S. (2021). Introducing zero trust by design: Principles and practice beyond the zero-trust hype. Advances in security, networks, and internet of things, 512-525.
- Capili, M. (2024). Simulation-Based Evaluation of Perimeter-Based and Zero Trust Security Implementation on Internet of Things (Doctoral dissertation, The George Washington University).
- Tiwari, S., Sarma, W., & Srivastava, A. (2022). Integrating Artificial Intelligence with Zero Trust Architecture: Enhancing Adaptive Security in Modern Cyber Threat Landscape. INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS, 9, 712-728.
- Zoting, S. (2024, October 17). Zero Trust Security market size to hit USD 161.60 BN by 2034. https://www.precedenceresearch.com/zero-trust-security-market.
- Ofili, B. T., Erhabor, E. O., & Obasuyi, O. T. (2025). Enhancing Federal Cloud Security with AI: Zero Trust, Threat Intelligence, and CISA Compliance. World Journal of Advanced Research and Review.
- Stolworthy RV, Morgan JC, Combe G, Woodruff NL, Stewart EM. Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology. Idaho National Laboratory (INL), Idaho Falls, ID (United States); 2024 Oct 22.
- Stafford V. Zero-trust architecture. NIST special publication. 2020 Aug;800(207):800-207.
- Radanliev P. Digital security by design. Security Journal. 2024 Dec;37(4):1640-79.
- Ali H. Reinforcement learning in healthcare: optimizing treatment strategies, dynamic resource allocation, and adaptive clinical decision-making. Int J Comput Appl Technol Res. 2022;11(3):88-104. doi:10.7753/IJCATR1103.1007.2399. World Journal of Advanced Research and Reviews, 2025, 25(02), 2377-2400.
- Echols M, Thomas B, Seckman K, Belcher S, Cybersecurity M, Transit RI. Cybersecurity Resilience Assessment Tool to Enhance Public Confidence in Transit. United States. Department of Transportation. Federal Transit Administration; 2023 Aug 1.
- Paul, F. (2023). AI-Powered Threat Detection in Hybrid and Multi-Cloud Environments: Overcoming Security Challenges.
- Anandharaj, N. (2024). AI-Powered Cloud Security: A Study on the Integration of Artificial Intelligence and Machine Learning for Improved Threat Detection and Prevention. J. Recent Trends Comput. Sci. Eng. (JRTCSE), 12, 21-30.
- Inaganti, A. C., Ravichandran, N., Nersu, S. R. K., & Muppalaneni, R. (2021). Cloud Security Posture Management (CSPM) with AI: Automating Compliance and Threat Detection. Artificial Intelligence and Machine Learning Review, 2(4), 8-18. Artificial Intelligence Improves Security in Hybrid Cloud.
- Blonder, R., & Feldman-Maggor, Y. (2024). AI for chemistry teaching: responsible AI and ethical considerations. Chemistry Teacher International, 6(4), 385–395. https://doi.org/10.1515/cti-2024-0014.
- Osasona, F., Farayola, O., Ayinla, B., Atadoga, A., Amoo, O., & Abrahams, T. (2024). REVIEWING THE ETHICAL IMPLICATIONS OF AI IN DECISION, MAKING PROCESSES. International Journal of Management & Entrepreneurship Research, 6(2), 322–335. https://doi.org/10.51594/ijmer.v6i2.773.
- Familoni, B. (2024). CYBERSECURITY CHALLENGES IN THE AGE OF AI: THEORETICAL APPROACHES AND PRACTICAL SOLUTIONS. Computer Science & IT Research Journal, 5(3), 703–724. https://doi.org/10.51594/csitrj.v5i3.930.
- Mylrea, M., & Robinson, N. (2023). Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25(10), 1429. https://doi.org/10.3390/e25101429.
- Jeyaraman, M., Balaji, S., Yadav, S., & Jeyaraman, N. (2023). Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus, 15(8). https://doi.org/10.7759/cureus.43262.
- Folorunso, A., Olawumi, T., Okonkwo, R., Adewumi, T., & Adewa, A. (2024). Impact of AI on cybersecurity and security compliance. Global Journal of Engineering and Technology Advances, 21(1), 167–184. https://doi.org/10.30574/gjeta.2024.21.1.0193
- Ahmadi, S. (2024). Zero Trust Architecture in Cloud Networks: Application, Challenges and Future Opportunities. Journal of Engineering Research and Reports, 26(2), 215–228. https://doi.org/10.9734/jerr/2024/v26i21083.
- Shahana, A., Johora, F. T., Mahmud, M. A. A., Farabi, S. F., Hasan, R., Akter, J., & Suzer, G. (2024). AI-Driven Cybersecurity: Balancing Advancements and Safeguards. Journal of Computer Science and Technology Studies, 6(2), 76–85. https://doi.org/10.32996/jcsts.2024.6.2.9.
- Sontan, A., & Samuel, S. (2024). The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities. World Journal of Advanced Research and Reviews, 21(2), 1720–1736. https://doi.org/10.30574/wjarr.2024.21.2.0607.
- Li, S., Iqbal, M., & Saxena, N. (2022). Future Industry Internet of Things with Zero-trust Security. Information Systems Frontiers, 26(5), 1653–1666. https://doi.org/10.1007/s10796-021-10199-5.
Enterprises face a critical security challenge when they deploy hybrid cloud systems because these systems
combine public cloud scalability with private cloud data control. Zero-trust security frameworks must be adopted because
traditional perimeter-based security methods no longer work in hybrid cloud environments with their dynamic and
decentralized nature. Every person’s system and device must prove their identity under the zero-trust model because no
entity should receive unconditional trust regardless of its location. The hybrid cloud environment demands advanced
security approaches because it handles massive amounts of data while facing complex modern cyber threats. This paper
investigates the implementation of artificial intelligence (AI) systems to boost zero-trust security protection within hybrid
cloud infrastructure. The research investigates present trends and upcoming directions to develop an extensive framework
which uses artificial intelligence for zero-trust security protection of hybrid cloud systems against modern cyber threats.
We analyze the advantages and obstacles and ethical aspects of implementing zero trust for AI together with its actual
usage in hybrid cloud systems. The research provides a complete method to use artificial intelligence for improving Zero
Trust security in hybrid cloud systems through analysis of present trends and future development possibilities. Such
measures will establish an active intelligent and strong defense mechanism against present and future cyber threats.
Keywords :
Hybrid Cloud, Public Cloud, Private Cloud, Zero Trust, Zero Trust Security Framework, Perimeter-Based Security, Decentralized Security, Cyber Threats, Artificial Intelligence (AI), Scalability, Flexibility, Resilient Defense, Security Challenges, Ethical Considerations, Future Developments.