Secure Multi-Agent AI Orchestration for Enterprise Workflows Across Distributed Cloud Environments with Zero Trust Architecture
DOI:
https://doi.org/10.15662/IJEETR.2026.0805006Keywords:
Multi-agent artificial intelligence, secure AI orchestration, Zero Trust Architecture, distributed cloud computing, enterprise workflow automation, identity and access management, least-privilege authorization, AI security governance, policy enforcement, cloud securityAbstract
The increasing adoption of distributed cloud environments has accelerated the integration of artificial intelligence into enterprise workflows, enabling intelligent automation, adaptive decision-making, and coordinated business operations. However, deploying multiple autonomous AI agents across heterogeneous cloud platforms introduces security challenges involving identity management, inter-agent communication, unauthorized access, data confidentiality, and operational accountability. Traditional security architectures often rely on perimeter-based defenses that are insufficient for protecting dynamic, distributed, and agent-driven enterprise systems. This research proposes a secure multi-agent AI orchestration framework integrating Zero Trust Architecture (ZTA) to strengthen access control, communication security, and workflow governance across distributed cloud environments. The framework coordinates specialized AI agents through policy-controlled orchestration, continuous identity verification, least-privilege authorization, encrypted communication, and contextual risk assessment. Each agent receives permissions according to its assigned responsibilities, resource requirements, and operational context. A centralized policy enforcement mechanism evaluates access requests and monitors interactions between agents, cloud services, application programming interfaces, and enterprise data resources. The proposed methodology incorporates architectural design, threat modeling, workflow simulation, security testing, and comparative performance evaluation. Metrics include unauthorized access prevention, policy violation detection, workflow completion time, communication latency, resource utilization, and scalability. The framework aims to balance intelligent automation with security enforcement while maintaining transparency, auditability, and operational resilience. This research establishes a foundation for trustworthy multi-agent orchestration in enterprise cloud ecosystems and provides an evaluation approach for assessing security effectiveness, computational overhead, and practical deployment feasibility.References
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