From Logs to Intelligence: Leveraging Data Science for Service Account Monitoring

Abstract

Service accounts (non-human credentials used to facilitate automation, system integrations, and machine-to-machine communication) play a vital role in today’s enterprise and cloud infrastructures. Given their often-elevated privileges and broad access scopes, these accounts have become high-value targets for cyber adversaries. However, conventional security monitoring tools frequently fall short in identifying misuse or lateral movement involving service accounts, largely due to their distinct and complex usage patterns. This paper presents a data-driven framework that leverages machine learning, big data analytics, and real-time anomaly detection to analyze multi-source log data and uncover suspicious or malicious service account behavior. Emphasis is placed on hybrid environments that span cloud and on-premises systems. We also examine key operational challenges, including model drift, scarcity of labeled data, and regulatory compliance. The proposed approach offers actionable insights and outlines strategies for integrating intelligent service account monitoring into broader security operations and incident response workflows.