HUMAN-CENTRIC CYBERSECURITY: ADDRESSING INSIDER THREATS WITH BEHAVIOR ANALYTICS

Abstract

As cybersecurity threats continue to evolve, insider threats have emerged as one of the most complex and damaging challenges for organizations. Unlike external attacks, insider threats originate from individuals within the organization who have authorized access, making detection and prevention particularly difficult. This paper explores a human-centric approach to cybersecurity that emphasizes the role of behavior analytics in identifying, mitigating, and preventing insider threats. By leveraging advanced machine learning algorithms and psychological profiling, behavior analytics enables real-time monitoring of user activities, flagging anomalous patterns that may indicate malicious intent or compromised credentials. The study highlights the integration of behavior analytics into existing cybersecurity frameworks, discusses ethical concerns, and evaluates its effectiveness in various sectors. Ultimately, this research advocates for a proactive, data-driven, and ethically sound strategy that aligns technology with human behavior to build resilient cyber defense systems.