Adaptive Zero Trust Security for AI Workloads in Modern Cloud Computing Enterprise Environments
DOI:
https://doi.org/10.15662/IJEETR.2026.0804008Keywords:
adaptive Zero Trust, artificial intelligence, AI workloads, cloud computing, enterprise security, cybersecurity, workload identity, least privilege, micro-segmentation, behavioral analytics, risk-based access control,, cloud security, machine learning security, data protection, continuous authenticationAbstract
The rapid integration of artificial intelligence (AI) into enterprise cloud environments has introduced new security challenges involving sensitive data, dynamic workloads, machine-learning models, application programming interfaces, distributed computing resources, and increasingly autonomous AI agents. Traditional perimeter-based security approaches are inadequate for protecting these environments because enterprise AI workloads frequently operate across multiple clouds, containers, microservices, APIs, edge resources, and external data platforms. This study investigates an adaptive Zero Trust security framework specifically designed for AI workloads in modern cloud computing enterprise environments. The proposed framework integrates continuous identity verification, least-privilege access control, workload identity, behavioral analytics, risk-based authorization, micro-segmentation, encryption, model security, and continuous monitoring. Unlike static security architectures, the proposed approach dynamically adjusts access privileges according to contextual risk indicators such as workload behavior, user identity, device posture, data sensitivity, model activity, network conditions, and detected anomalies. The research methodology combines systematic literature analysis, security architecture design, prototype implementation, controlled attack simulations, and quantitative evaluation. Security effectiveness is assessed through unauthorized-access prevention, anomaly-detection accuracy, policy enforcement, response time, false-positive rate, lateral-movement resistance, and operational overhead. The research aims to demonstrate that adaptive Zero Trust can provide a stronger security foundation for enterprise AI workloads while maintaining scalability, availability, and operational efficiency.
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