Trustworthy Autonomous Decision Automation through Neuro-Symbolic AI in Regulated Multi-Cloud Systems

Abstract

Regulated enterprises increasingly operate across multiple cloud platforms to achieve scalability, resilience, flexibility, and business continuity while complying with strict legal, regulatory, security, and governance requirements. However, autonomous decision automation in multi-cloud environments remains challenging because conventional artificial intelligence models may provide highly accurate predictions without producing transparent, auditable, or policy-compliant decisions. This research proposes a trustworthy autonomous decision automation framework based on Neuro-Symbolic Artificial Intelligence (NSAI), integrating neural learning capabilities with symbolic reasoning, explicit enterprise policies, regulatory constraints, and contextual knowledge. The proposed framework enables autonomous agents to interpret heterogeneous multi-cloud data, detect operational and security conditions, reason over formalized rules, evaluate alternative actions, and execute decisions while maintaining traceability and governance. Neural models provide adaptive pattern recognition for dynamic cloud telemetry, while symbolic components enforce regulatory requirements, business policies, access constraints, and risk controls. A policy-aware orchestration layer coordinates autonomous decisions across public, private, and hybrid cloud resources. The methodology combines multi-cloud architecture design, regulatory knowledge modeling, synthetic enterprise datasets, machine-learning development, symbolic rule construction, agent-based decision automation, and controlled experimental validation. Performance is evaluated using decision accuracy, policy-compliance rate, explainability, false-decision rate, response time, audit completeness, and resource efficiency. The proposed framework aims to establish trustworthy autonomous decision-making that combines AI adaptability with deterministic governance, thereby supporting secure, explainable, accountable, and regulation-aware enterprise automation.