Proactive AI-Driven Security Analytics for Enterprise Cloud Infrastructure Using Behavioral Threat Detection and Graph Learning
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605021Keywords:
Proactive security analytics, artificial intelligence, enterprise cloud infrastructure, behavioral threat detection, graph learning, graph neural networks, cybersecurity, anomaly detection, threat intelligence, attack path analysis, predictive risk assessment, cloud securityAbstract
Enterprise cloud infrastructure faces increasingly sophisticated cyber threats that exploit distributed workloads, compromised identities, misconfigured resources, and complex interactions among cloud services. Conventional security monitoring systems primarily depend on predefined rules and isolated event analysis, limiting their ability to identify previously unknown attacks and coordinated malicious activities. This study proposes a proactive AI-driven security analytics framework that integrates behavioral threat detection with graph learning to improve threat identification and risk assessment across enterprise cloud environments. The framework collects telemetry from identity services, applications, network connections, virtual machines, containers, and cloud audit logs to establish behavioral profiles and construct dynamic graphs representing relationships among users, resources, workloads, and security events. Machine learning techniques identify deviations from established behavioral patterns, while graph learning models analyze relational structures and temporal dependencies to detect suspicious activity sequences and potential attack paths. A contextual risk assessment mechanism combines anomaly scores, graph-based indicators, asset criticality, privilege levels, and threat intelligence to prioritize emerging threats. The research methodology adopts a design-oriented approach supported by controlled experiments and quantitative performance evaluation. Detection precision, recall, F1-score, false-positive rate, detection latency, and graph-analysis efficiency are used to assess effectiveness. The framework also emphasizes explainability, privacy protection, adaptive learning, and human-supervised response. The proposed approach aims to enhance proactive threat detection, improve security visibility, and support timely, context-aware defense across complex enterprise cloud infrastructures
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