Automated Compliance Monitoring for Enterprise Cloud Security Using Explainable AI and Policy-as-Code Frameworks
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Abstract
Enterprise cloud environments require continuous compliance monitoring to ensure that infrastructure configurations, access controls, data protection mechanisms, and operational practices remain aligned with organizational policies and regulatory requirements. Traditional compliance assessments often rely on periodic audits, manual inspections, and static rule-based checks, which may fail to identify violations promptly in dynamic cloud infrastructures. This study investigates an integrated framework for automated cloud compliance monitoring using explainable artificial intelligence (XAI) and policy-as-code mechanisms. The proposed framework combines continuous cloud telemetry collection, automated policy evaluation, AI-assisted anomaly identification, compliance risk classification, and evidence-based reporting to improve compliance visibility and operational accountability. Policy-as-code expresses organizational requirements as machine-readable rules that can be validated during infrastructure deployment and continuously evaluated against deployed resources. Explainable AI supports the interpretation of complex compliance findings by identifying relevant configuration attributes, contributing risk factors, and reasons for potential violations. The research methodology adopts a design-oriented approach supported by controlled experiments and quantitative performance evaluation. Key evaluation metrics include compliance detection accuracy, precision, recall, false-positive rate, detection latency, policy evaluation performance, and explanation quality. The framework also incorporates audit trails, policy versioning, human oversight, and controlled remediation to support trustworthy compliance management. The proposed approach aims to reduce manual assessment efforts, improve the timely identification of compliance violations, and strengthen governance across enterprise cloud platforms while maintaining transparency, consistency, and accountability in automated compliance decisions.
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