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Analysis Study Research Paper | Computer and Mathematical Sciences | Volume 15 Issue 4, April 2026 | Pages: 700 - 703 | United States
Multi-Cloud Security Assessment for AI-Augmented Enterprise Environments
Abstract: The dynamic digital transformation of enterprises has introduced distributed architectures, adaptive Artificial Intelligence/Machine Learning (AI/ML) systems, and large-scale data ecosystems spanning across multiple cloud providers. Along with these associated technologies the need for enhanced scalability, operational efficiency, and automated decision-making have increased. Simultaneously expanding the attack surface and introducing novel vulnerabilities including adversarial machine learning, model extraction, data poisoning, prompt injection, and cross-cloud data exposure. Traditional cybersecurity frameworks, originally designed for perimeter-based and deterministic enterprise systems, are insufficient for addressing the dynamic, probabilistic, and distributed risk landscape of such AI-augmented multi-cloud environments. This paper proposes a Multi-Cloud Security Assessment (MCSA) framework for unified enterprise security architecture that integrates existing security controls, human-centric safeguards, and regulatory compliance mechanisms. The framework identifies potential security risks and vulnerabilities while aligning with the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0 [1]. The proposed assessment is structured across five NIST CSF 2.0 functions: Identify, Protect, Detect, Respond, and Recover. The findings demonstrate that integrating Zero-Trust Architecture (ZTA) [5], defense-in-depth strategies [17], AI lifecycle security controls, and privacy-by-design methodologies [18] establishes digital trust across multi-cloud enterprises. A threat model, control mapping table, and simulated case study validate the proposed framework's effectiveness against real-world attack scenarios.
Keywords: Multi-cloud security, Zero Trust Architecture, AI/ML security, NIST CSF 2.0, adversarial machine learning, prompt injection, data poisoning, federated identity, defense-in-depth, incident response
How to Cite?: Manonmayi Vedam, Arungopan Gopakumar, "Multi-Cloud Security Assessment for AI-Augmented Enterprise Environments", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 700-703, https://www.ijsr.net/getabstract.php?paperid=SR26331105045, DOI: https://dx.dx.doi.org/10.21275/SR26331105045