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Zero-Trust Cloud Security Architectures with AI-Orchestrated Policy Enforcement for U.S. Critical Sectors
Open accessAbstract
As U.S. critical sectors such as finance, healthcare, and defense accelerate their transition into cloud-native infrastructures, the need for robust and adaptive cybersecurity frameworks has become a matter of national urgency.Traditional perimeter-based models have proven insufficient against sophisticated cyberattacks, particularly supply chain compromises exemplified by SolarWinds, which exploit trusted channels to bypass conventional defenses.Addressing this challenge requires a paradigm shift toward zero-trust principles, where continuous authentication, contextual verification, and strict least-privilege policies are fundamental to ensuring system integrity.This paper proposes a zero-trust cloud security architecture enhanced by AI-orchestrated policy enforcement and microsegmentation to safeguard high-value assets in critical infrastructure.At a broad level, the framework combines identity-centric security with automated decision-making models that continuously monitor user behavior, system activity, and network flows.AI-driven orchestration enables real-time privilege adjustments, proactive anomaly detection, and automated policy enforcement across distributed cloud environments.By embedding intelligence into access and workload controls, the system not only prevents lateral movement by malicious actors but also adapts dynamically to evolving threat landscapes.Focusing on application within the U.S. context, this work demonstrates how the architecture supports resilience against insider threats, ransomware, and supply chain attacks targeting essential services.The framework establishes a scalable, national-security-grade model capable of supporting compliance with federal cybersecurity mandates while maintaining operational efficiency.Narrowing the scope, the study underscores that the integration of zero-trust and AI is not merely a technical upgrade but a strategic necessity for securing critical infrastructures that underpin national stability.
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“which are insufficient given the scale and rapid iteration of generative models”
From Designing automated audit mechanisms to evaluate compliance of generative AI platforms with federal authorship and ownership disclosure requirements. (2023) · cites this paper as [6]
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“current compliance enforcement relies heavily on voluntary disclosure and manual auditing”
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“Without mechanisms to systematically document the conditions under which generative outputs are produced, attribution becomes speculative rather than evidentiary [6].”
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“However, current compliance enforcement relies heavily on voluntary disclosure and manual auditing, which are insufficient given the scale and rapid iteration of generative models.”
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“Existing approaches typically rely on manual documentation, platform self-reporting, or reactive enforcement processes that occur only after disputes arise, producing inconsistent and insufficient compliance outcomes [6].”
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Cited by
Sorted by citations
Omogiate, P. M. (2023) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2023.10.2.1099 · Cited by 3
Gade, S. R., Medikonda, N. B., Kothakapu, S. C., Addula, S. R., Betheli, A. R., & Nookala, P. R. (2025) · International Conference of Advances in Engineering and Computing Technologies for Sustainable Development (AECTSD) · doi:10.1109/aectsd65988.2025.11411402 · Cited by 1
Das, R., Khan, S. Z., Robbani, M. S., Islam, M. R., & Islam, M. I. (2026) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee69242.2026.11524269
Performance-Aware Zero-Trust Architecture for Secure and Scalable Multi-Cloud Computing
El-Anqoudy, N. A. N., Kushwaha, A. S., Chacko, D., & Kumar, L. (2026) · International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC) · doi:10.1109/icisessc68634.2026.11542634
Dynamic Access Control and Privacy Compliance Strategies in a Zero-Trust Data Security Architecture
Sun, L., Zhang, L., Yuan, Y., Xue, J., Lv, Y., & Yue, Y. (2026) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit70033.2026.11557827
Piridi, S., Asundi, S., Kamineni, S., & Dadi, C. B. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00284-8
Metadata
- Venue
- International Journal of Science and Engineering Applications
- Published
- 2025-10-03
- ISSN
- 2319-7560 (ISSN-L)
- OpenAlex
- W4414796036
- Cited by
- 5
- References
- 0
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Science and Engineering Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
- Keywords
- zero-trust security, cloud security architecture, micro-segmentation, critical infrastructure protection, least privilege, continuous authentication, anomaly detection, insider threats, ransomware, supply chain attacks, identity-centric security
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