Published: Oct 17, 2025

A case for AI-enabled insider threat detection

Insider threats are among the hardest security challenges to detect because they originate from users with legitimate access. For a major client operating in a high-security environment, NCS designed and deployed an AI-enabled insider threat detection system that analysed user behaviour across multiple data sources to flag anomalies early. The solution delivered proactive visibility, faster detection, and improved response accuracy, achieving 85% detection accuracy while processing several terabytes of logs.
 

The challenge

The client, a regulated organisation with strict confidentiality and access protocols, faced increasing insider risk. Existing monitoring tools could not differentiate normal from suspicious user activity, leaving visibility gaps across access logs, privileged accounts, and team structures. The challenge was to create a unified, intelligent system that could analyse vast volumes of behavioural data and highlight potential threats before they escalated.
 

NCS implemented a tailored, multi-layered detection system that combined machine learning and rule-based analytics.

• Data integration: Aggregated IAM, PAM, and access logs alongside contextual team data to build a holistic view of user activity.
• Anomaly detection model: Established behavioural baselines and flagged deviations across time.
• Complementary engines:
  - Machine learning models adapted continuously to evolving patterns.
  - Rule-based logic ensured consistency and automated alerting.
• Operational enablement: Assigned dynamic risk scores to focus investigation on the most critical anomalies.

This hybrid design balanced adaptability with reliability, ensuring detection precision without compromising operational efficiency.


Figure 1: System Block Diagram of the NCS Insider Threats Case Study.
 

The deployed system processed several terabytes of log data and achieved 85% accuracy in identifying anomalous behaviour. The client gained early-warning capability for potential insider threats, improving both response time and resource prioritisation. The scalable framework also positioned the organisation to integrate new AI models seamlessly as threats evolve.

The project marked the client’s first AI-enabled insider threat system transforming reactive monitoring into proactive defence. By turning multi-source data into actionable intelligence, NCS helped the organisation stay ahead of internal risks while maintaining compliance and operational confidence.