Driven Observability for Predictive Cyber Resilience and Risk Management in Distributed Enterprise Cloud Platforms
DOI:
https://doi.org/10.15662/IJARCST.2024.0705019Keywords:
AI-driven observability, predictive cyber resilience, cloud security, enterprise cloud platforms, risk management, machine learning, anomaly detection, distributed systems, cybersecurity analytics, predictive security, cloud observabilityAbstract
The increasing complexity of distributed enterprise cloud platforms has created a pressing need for security approaches that extend beyond conventional monitoring and reactive incident detection. Modern cloud environments incorporate virtual machines, containers, microservices, APIs, serverless workloads, databases, identity services, and distributed networks, producing extensive telemetry across heterogeneous infrastructure. AI-driven observability provides an opportunity to transform this telemetry into predictive cyber-resilience intelligence by correlating operational, security, application, and infrastructure signals. This study investigates an AI-driven observability framework for predictive cyber resilience and risk management across distributed enterprise cloud platforms. The proposed approach integrates logs, metrics, traces, network events, identity activity, configuration changes, vulnerability information, and workload behavior into a unified analytical architecture. Machine learning and deep-learning techniques are employed for anomaly detection, behavioral profiling, risk prediction, failure forecasting, and early identification of cyber threats. Temporal modeling and graph-based analysis are incorporated to capture dependencies among users, services, workloads, and infrastructure resources. The methodology evaluates predictive accuracy, anomaly-detection performance, false-positive rates, detection lead time, risk-calibration quality, resilience indicators, and computational scalability. The study also examines concept drift, incomplete telemetry, workload migration, adversarial manipulation, and explainability. The proposed framework seeks to shift enterprise cloud security from reactive monitoring toward continuous predictive resilience by identifying emerging risks before they develop into significant incidents and supporting prioritized, evidence-based risk management across dynamic distributed cloud infrastructures.
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