Designing a Novel Insider Threat Model for Enhanced Cybersecurity - Manas Kumar Yogi (International Journal of Information Security Engineering, 2023)
Designing a novel insider threat model is a critical imperative in the realm of cybersecurity. As organizations face an ever-expanding threat landscape, insider threats, whether deliberate or inadvertent, present a formidable challenge to the safeguarding of sensitive data and critical assets. This abstract encapsulates the significance, challenges, and innovations inherent in crafting an effective insider threat model for enhanced cybersecurity. The necessity for novel insider threat models arises from the recognition that traditional security measures often overlook the dangers posed by trusted insiders. This paper explores the multifaceted domain of designing such models, emphasizing their proactive nature and adaptability to evolving security threats. The complexities of this endeavor are magnified by several challenges, ranging from acquiring high-quality data and maintaining compliance with privacy regulations to addressing false positives and combating evolving attack vectors. Additionally, the model's efficacy depends on a deep understanding of contextual information, user behavior profiling, and the ability to differentiate between normal and anomalous activities. It also requires striking a delicate balance between security and privacy, respecting ethical and legal standards while gaining the trust of employees and stakeholders. Innovations in insider threat modeling encompasses a comprehensive approach, integrating advanced machine learning algorithms, user and entity behavior analytics, and adaptive learning to create a dynamic defense against insider threats. This paper underscores the necessity of continuous improvement, collaboration between experts from diverse domains, and awareness of evolving threats and best practices.
Keywords: Cybersecurity; Insider threat; Privacy; malicious; safeguard; threat model