Abstract :

Insider Threats Represent A Complex Cybersecurity Challenge Because Individuals With Legitimate Access To Organizational Systems Can Potentially Misuse Their Privileges, Intentionally Or Unintentionally, To Compromise Information Assets. Traditional Security Mechanisms Based Primarily On Authentication, Access Control, Signatures, And Predefined Rules May Have Difficulty Identifying Subtle Deviations In Legitimate User Activity. Artificial Intelligence (AI) And Behavioural Analytics Provide An Opportunity To Address This Challenge By Continuously Analysing User And Entity Activities And Identifying Patterns That Differ From Established Behavioural Baselines. Recent Research Highlights The Use Of Supervised, Unsupervised, Deep-learning, Anomaly-detection, And Hybrid Approaches For Insider-threat Detection, While Also Identifying Persistent Challenges Involving Class Imbalance, False Positives, Limited Labelled Data, And Interpretability. This Article Examines An AI-driven Approach In Which Behavioural Data From Authentication Systems, Endpoints, Networks, Applications, File-access Systems, And Data-transfer Activities Are Analysed To Identify Potentially Suspicious Patterns. The Proposed Approach Combines Behavioural Profiling, Feature Extraction, Anomaly Detection, Machine-learningbased Risk Assessment, And Human-led Investigation. The Study Uses A Conceptual And Secondary Research Methodology To Examine Existing Literature And Develop A Framework For AI-assisted Insider-threat Detection. The Findings Indicate That Behavioural Analytics Can Enhance The Identification Of Subtle And Evolving Anomalies, Particularly When Multiple Behavioural Indicators Are Analysed Together. However, AI-based Detection Should Complement Rather Than Replace Human Investigation And Organizational Security Controls. Privacy, Explainability, Data Quality, Model Bias, False Positives, And Adversarial Adaptation Remain Important Considerations. The Study Proposes A Human-centred AI Framework For Improving Proactive Insider-threat Detection While Maintaining Appropriate Cybersecurity Governance.

Published:

23-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

344 - 351

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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