Autonomous Multi-Cloud Governance through Intelligent Agentic AI and Dynamic Policy Enforcement
DOI:
https://doi.org/10.64235/45gd145nKeywords:
Multi-cloud governance, agentic AI, autonomous agents, dynamic policy enforcement, cloud security, policy automation, artificial intelligence, compliance management, cloud orchestration, intelligent automationAbstract
Multi-cloud environments have become increasingly important for organizations seeking scalability, resilience, cost optimization, and flexibility across heterogeneous cloud providers. However, managing governance across multiple cloud platforms introduces significant challenges related to security, compliance, configuration consistency, identity management, resource optimization, and policy enforcement. Traditional governance approaches largely depend on static rules, manually configured controls, and centralized monitoring mechanisms, which may be inadequate for highly dynamic cloud infrastructures. This paper proposes an autonomous multi-cloud governance framework based on intelligent agentic artificial intelligence and dynamic policy enforcement. The proposed approach employs autonomous AI agents capable of continuously observing cloud environments, interpreting organizational policies, identifying deviations, reasoning about appropriate responses, and executing corrective actions across heterogeneous cloud platforms. A dynamic policy layer translates governance requirements into machine-enforceable controls while adapting enforcement decisions according to contextual information such as workload characteristics, risk levels, regulatory requirements, and operational conditions. The research methodology combines architectural design, scenario-based experimentation, quantitative evaluation, and comparative analysis. Key evaluation dimensions include policy-compliance accuracy, detection latency, remediation effectiveness, false-positive rates, resource overhead, and cross-cloud consistency. The proposed framework aims to establish a closed governance loop in which perception, reasoning, policy evaluation, action, and continuous learning operate together. Such an approach can strengthen governance automation while reducing administrative effort and improving the responsiveness of multi-cloud security and compliance management.
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