AI-Driven Anomaly Detection in IAM Systems for Proactive Threat Mitigation

Identity and Access Management (IAM) systems represent a foundational component of modern enterprise security architecture. Their primary role is to regulate how identities—both human and machine—gain access to critical systems, applications, and data assets. However, despite significant evolution in IAM tools, organizations continue to face a rapidly changing threat landscape that includes sophisticated cyberattacks, insider threats, and credential misuse. Static, rule-based IAM approaches often fail to detect subtle indicators of compromise or adapt to novel attack patterns, leaving gaps in enterprise defenses. Artificial Intelligence (AI) offers a transformative opportunity to enhance IAM capabilities through continuous learning and intelligent anomaly detection. AI-driven IAM leverages advanced analytics, machine learning (ML), and a contextual understanding of identity behaviors to surface anomalous activities that may indicate emerging threats proactively. By incorporating behavioral baselining, unsupervised anomaly detection, and contextual risk scoring, AI models can move beyond traditional policy enforcement and evolve toward predictive security postures. This paper presents an AI-driven framework for anomaly detection in IAM systems aimed at proactive threat mitigation. The framework integrates with existing IAM platforms, enriches detection with contextual signals, continuously improves through feedback, and supports automated threat response. It addresses key challenges, including high falsepositive rates, explainability, and integration complexity. Our proposed solution demonstrates the potential to strengthen enterprise cyber resilience by identifying and responding to threats before they cause significant damage. The paper also highlights areas for future research, including supply chain identity risks, cross-domain behavior analysis, and explainable AI in IAM.