Insider threats remain one of the most complex security challenges because they originate from trusted identities with legitimate access. Traditional perimeter-based controls and static rule engines are no longer sufficient to detect subtle misuse, compromised accounts, or risky behavior.

This session explores how to build a unified behavioral view of users by correlating identity context, access patterns, device posture, network activity, and sensitive data interactions. It demonstrates how AI-driven models create dynamic baselines of normal behavior and detect anomalies in real time.

Attendees will learn how to reduce false positives, move from reactive incident response to proactive risk management, and design people-centric security monitoring architectures suitable for modern engineering environments.