This research demonstrates how Large Language Models can be leveraged to detect insider threats through synthetic data analysis, addressing a critical security challenge for organizations.

• Identifies insider threats by analyzing anonymous workplace reviews and communications
• Provides a scalable approach to security monitoring without compromising ethics
• Balances effective threat detection with privacy considerations
• Offers practical implementation guidance for security teams

For security professionals, this research presents a novel approach to proactively identify potential insider risks before they escalate into security breaches, potentially saving organizations from significant financial and reputational damage.

Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs