Mitigating Insider Threats through Behavioural Analytics and Cybersecurity Policies

Abstract

The insider threat is one of the most prominent risks in today's cybersecurity environment, primarily due to the privileged access that insiders have inside the organization. These threats could stem from malicious intent, negligence, or through compromised credentials, therefore being hard to detect using any typical methods by cybersecurity. It will explore how a combination of behavioral analytics and the overall cybersecurity policies can be used to mitigate these insider threats effectively. Behavioural analytics, which is based on machine learning models, monitors and predicts the anomaly that could be represented by anomalous user behavior that may be a potential insider threat. Meanwhile, cybersecurity policy represents an active defense layer in outlining guidelines of access control data handling and employee behavior. This would therefore allow an organization to identify and block insider threats in real time through the minimization of false positives as well as enhancing its general security posture in an organizational setting upon integration of the two approaches. This real-world insider threat dataset the author uses goes on to further illustrate just how well machine learning algorithms can and do identify suspicious activities-cases in point being Random Forest and Support Vector Machines. The authors continue to show a much stronger evaluation of the impact of cybersecurity policies in counteracting insider threats by showing some of the flaws, such as weighing the privacy concerns against achieving employee confidence. This study gives real actionable insights into how organizations can strengthen their defense against insider threats and consequently reduce the risk of data breaches, intellectual property theft, and sabotage.