Contextual AI-Based Security Compliance Analysis across Enterprise Cloud Systems and Distributed Infrastructure
DOI:
https://doi.org/10.15662/IJEETR.2024.0605026Keywords:
Contextual artificial intelligence, security compliance, enterprise cloud systems, distributed infrastructure, regulatory compliance, machine learning, cloud security governance, continuous compliance monitoring, natural language processing, risk assessment, policy enforcement, explainable AIAbstract
The increasing adoption of cloud computing, distributed infrastructure, and interconnected enterprise applications has created significant challenges for maintaining consistent security compliance across complex information technology environments. Traditional compliance assessment approaches frequently depend on periodic audits, predefined rules, and manual verification, limiting their ability to identify contextual risks and continuously evaluate changing configurations. This study examines a contextual artificial intelligence-based framework for security compliance analysis across enterprise cloud systems and distributed infrastructure. The proposed approach integrates cloud telemetry, configuration records, identity and access management information, vulnerability assessments, security policies, and regulatory requirements to identify compliance deviations and evaluate their organizational significance. Natural language processing techniques are used to interpret policy documents and map compliance requirements to technical controls, while machine learning methods support anomaly detection, risk classification, and prioritization of compliance violations. Contextual factors, including asset criticality, data sensitivity, exposure, access privileges, and service dependencies, are incorporated to distinguish high-impact violations from lower-risk deviations. The methodology evaluates detection precision, recall, F1-score, assessment latency, contextual prioritization quality, and control-mapping accuracy. It also considers explainability, data privacy, regulatory ambiguity, and cross-platform interoperability. The proposed framework provides a methodological foundation for continuous, risk-informed compliance monitoring, enabling enterprises to improve visibility, reduce manual assessment effort, and strengthen governance across heterogeneous cloud and distributed environments while preserving human oversight for consequential compliance decisions
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