Leveraging Identity and Access Management to Mitigate Insider Threats

This paper explores the essential function of Identity and Access Management (IAM) in reducing insider threats and enhancing the cybersecurity resilience of organisations. Insider threats, whether deliberate or unintentional, pose significant challenges to data integrity, operational stability, and regulatory compliance. With the growing adoption of cloud-based infrastructure and remote work models, identity and access management has transformed from a static access-control mechanism into a dynamic, intelligence-driven framework. The research employs a qualitative interpretive methodology, integrating contemporary academic and industry literature from 2022 to 2025 to examine how emerging technologies such as Artificial Intelligence (AI), Zero Trust Architecture (ZTA), and blockchain enhance the effectiveness of IAM. The findings indicate that AI-enhanced IAM frameworks significantly improve anomaly detection and privilege management, whereas blockchain provides transparency and immutability for access governance. Moreover, following international standards such as NIST SP 800-207 and ISO/IEC 27001 ensures compliance and facilitates interoperability across sectors. The results highlight that successful IAM implementation involves not only technology but also a blend of human, procedural, and regulatory factors. The paper concludes that future advancements in IAM will rely on integrating explainable AI, decentralised identity models, and quantum-resilient architectures to ensure adaptive, ethical, and secure identity governance within evolving digital ecosystems. As this study employs a qualitative, interpretive methodology based exclusively on secondary sources, the findings are conceptual. Empirical validation through longitudinal case studies and controlled deployments constitutes an important direction for future research.