AI-Driven Security Automation for Continuous Compliance Monitoring in Regulated Cloud Environments
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P112Keywords:
AI Security Automation, Continuous Compliance, Cloud Security, Regulatory Framework, Autonomous Monitoring, Threat Detection, Machine Learning, Cloud GovernanceAbstract
Cloud computing has revolutionized modern IT infrastructure by providing scalable, flexible, and cost-effective computing resources. However, as cloud adoption expands, ensuring continuous compliance with regulatory frameworks such as GDPR, HIPAA, ISO 27001, and PCI DSS has become increasingly complex. Traditional manual compliance methods are insufficient for dynamically scaling cloud environments due to the high volume of data, heterogeneous services, and rapidly changing threat landscapes. AI-driven security automation has emerged as a next-generation solution capable of autonomous monitoring, intelligent threat detection, and proactive compliance enforcement. This paper explores the design, implementation, and effectiveness of AI-driven security automation frameworks for continuous compliance monitoring in regulated cloud environments. The research highlights three primary contributions: first, the integration of machine learning (ML) and artificial intelligence (AI) for automated compliance rule enforcement; second, a detailed methodology for mapping regulatory requirements into actionable security policies; and third, performance evaluation using a simulated multi-cloud environment to demonstrate real-time monitoring, anomaly detection, and automated remediation. The proposed AI-driven framework combines supervised and unsupervised learning techniques for predictive risk assessment, continuous log analysis, and behavioral anomaly detection. Reinforcement learning agents are employed to adaptively optimize security policies according to evolving regulatory updates and system changes. We introduce a layered security architecture comprising data collection, AI-based analytics, compliance verification, and automated remediation modules. The framework leverages Natural Language Processing (NLP) for parsing textual compliance guidelines into structured rules, which are then codified into automated policies. An advanced decision engine prioritizes risk events based on severity, potential impact, and regulatory criticality. Quantitative experiments show that AI-driven automation reduces policy violations by 65%, shortens response time to security events by 70%, and achieves near-real-time compliance reporting with minimal human intervention. Furthermore, the paper investigates challenges such as model explainability, regulatory policy ambiguity, and integration complexity in multi-cloud scenarios. Case studies are provided for healthcare (HIPAA compliance), financial services (PCI DSS compliance), and general enterprise cloud governance (ISO 27001). Finally, we discuss the implications of AI-driven continuous compliance for future cloud security practices, highlighting how automation, when combined with intelligent analytics, can transform cloud governance from reactive to proactive models. The study concludes that AI-driven security automation not only enhances regulatory adherence but also improves operational efficiency, reduces risk exposure, and provides actionable insights for decision-makers in large-scale regulated cloud environments
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References
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