Volume 2 - Issue 3, May - June 2026

Internal sources ie insiders tend to threaten organizations and Because of this, they create a major headache for organizations. It is quite a challenge to find such threats as the perpetrators of the breaches are the same people we rely on - employees, contractors or business partners. Threats can either be intentional like a person stealing data, misusing a system, or unintentional when a person makes a mistake or their credentials are compromised. In the light of digital technology proliferation, business companies have to figure out the ways to level up the detection of insider threats. In this paper, the focus is on one of the approaches that have been applied to detecting these threats, that is, user behavior analysis. The data extracted from server application databases and network device logs tell of user activities like login, file access, and network usage. By analyzing logs, you can tell what is a normal behavior and what actions are out of the norm or anomalous. Our approach comprises six stages: data collecting, data quality checking, key elements extraction, behavioral analysis, suspicious activity detection, and risk assessment. To be precise and aware of users' movements over time, we employ machine learning and statistical methods. Because of this, we can differentiate between normal and malicious behaviors. The research results show that behavior analysis and log reviews are a very good help in discovering insider threats. It supports organizations in their effort of securitization, incident response as well as protection of confidential information and essential digital resources. Really insider threats are an issue of great concern and for that reason, organizations must be able to detect them. That is the solution we offer on that. Finally, we consider that simply put the reason why insider threats still exist is the lack of proper disinfecting tools and techniques.Keywords: anomaly detection, cybersecurity, insider threats, log analysis, user behavior analytic.

Atharv Jaitapkar, Jayesh Shinde,"Insider Threat Identification Through User Behavior Analytics and System Log Monitoring" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(955-964) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.