Autonomous Artificial Intelligence Techniques for Secure Enterprise Workflow Orchestration
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I3P103Keywords:
Autonomous Artificial Intelligence, Enterprise Workflow Orchestration, Cybersecurity, Intelligent Automation, Reinforcement Learning, Explainable Ai, Secure Cloud Infrastructure, Zero-Trust Security, Workflow Optimization, Enterprise ComputingAbstract
The growing complexity of enterprise digital ecosystems has increased the need for intelligent workflow orchestration capable of autonomously managing operations, cybersecurity, resource allocation, and service continuity. Autonomous Artificial Intelligence (AAI) has emerged as a key technology for enabling adaptive, secure, and self-regulating enterprise workflows across hybrid cloud, edge, and distributed environments. This study examines the application of advanced AI techniques, including reinforcement learning, federated learning, deep neural networks, graph-based intelligence, explainable AI, and autonomous agents, in enterprise workflow management. The research evaluates how AI-driven orchestration improves threat detection, anomaly prediction, adaptive scheduling, compliance monitoring, and resource optimization. It also identifies limitations of traditional orchestration systems and highlights challenges related to scalability, transparency, interoperability, trust, and cybersecurity resilience. A conceptual framework integrating intelligent monitoring, predictive analytics, dynamic policy enforcement, and zero-trust security is proposed. Findings indicate that autonomous AI enhances workflow adaptability, operational efficiency, cyber resilience, and decision-making in multi-cloud enterprise environments. However, challenges related to governance, explainability, ethics, and trust remain significant areas for future research. The study provides insights into the design and implementation of secure, intelligent workflow orchestration systems and offers a roadmap for next-generation enterprise AI solutions.
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