Data-Driven Risk Prioritization in Cloud Security Operations Centers

Cloud Security Operations Centers (Cloud SOCs) face a deluge of security alerts from distributed cloud environments, making it difficult to distinguish high-impact risks from noise. Traditional rule-based prioritization often fails to adapt to evolving threat landscapes, leading to analyst fatigue and delayed responses. This paper proposes a data-driven risk prioritization framework that leverages contextual telemetry, machine learning, and business impact analytics to dynamically rank alerts by potential risk. Drawing on modern data engineering and cloud-native observability pipelines, the paper presents architectural insights and operational strategies for next-generation Cloud SOCs [1].

Data Science Cloud Computing Computer Networks