How a New AI Tool Could Revolutionize Insider Threat Defense Testing
Insider threats pose significant challenges for security teams, as these attacks originate from individuals who already possess legitimate access to sensitive information. While security professionals are acutely aware of the risks associated with insider threats, they often lack the necessary data to train systems capable of identifying subtle patterns indicative of malicious behaviour. To address this issue, a research team has developed Chimera, a system that utilises Large Language Model (LLM) agents to simulate both normal and malicious employee activities within enterprise environments. The primary objective of Chimera is to tackle the critical problem of insufficient realistic and shareable datasets for insider threat detection.
Chimera employs LLM-based multi-agent simulations to create authentic organisational scenarios. Each LLM agent represents a specific employee, complete with defined roles, responsibilities, and basic personality traits. These agents interact, collaborate, and adhere to daily routines that encompass meetings, emails, and project tasks. Some agents are designated as insider attackers, executing malicious activities while maintaining their regular duties to evade detection. The system is adaptable to various enterprise profiles, including technology companies, financial institutions, and medical organisations. By automatically configuring the software environment, access controls, and communication channels, Chimera records both benign and attack-related activities across six log types. The resulting ChimeraLog dataset comprises approximately 25 billion log entries, encompassing 15 insider attack scenarios based on real-world incidents. This innovative approach allows security teams to generate realistic insider threat scenarios without compromising sensitive internal data.
Categories: Insider Threat Detection, Simulation Technology, Data Privacy
Tags: Insider Threats, Detection, Simulation, LLM Agents, Dataset, Malicious Behavior, Security Teams, Organizational Scenarios, Log Entries, Customization