Privileged User Behavior Analytics (PUBA) for Insider Threat Detection
Sri kanth Mandru · Journal of Artificial Intelligence Machine Learning and Data Science · 2024
Privileged User Behavior Analytics (PUBA) has emerged as a vital element in the information and resource security architecture of modern organizations, focusing on detecting unusual behaviors and mitigating insider threats.In an evolving cybersecurity landscape where internal risks pose significant threats, PUBA employs advanced machine learning algorithms and behavioral analytics to scrutinize user activity patterns, monitor access requests, and analyze system interactions.This paper assesses the efficacy of PUBA methodologies by leveraging these technologies, highlighting their role in identifying credential misuse and unauthorized access.The evaluation underscores the importance of PUBA in preemptively identifying potential insider threats, thereby enhancing the overall security posture of organizations.By analyzing real-time data and establishing behavioral baselines, PUBA tools can detect anomalies indicative of malicious or negligent insider activities.This study aims to demonstrate how effectively PUBA solutions can safeguard critical organizational assets, reduce financial losses, and maintain regulatory compliance.Furthermore, it explores the integration of PUBA within existing security frameworks, emphasizing its proactive approach to threat detection and risk management.The findings advocate for the broader adoption of PUBA solutions as a cornerstone of comprehensive cybersecurity strategies.