AI-Integrated Identity and Access Management: Advancing Cybersecurity through Intelligent Access Control and Continuous Authentication

The rapid development of cyber threats and the increasing complexity of IT infrastructures have revealed large weaknesses in traditional Identity and Access Management (IAM) solutions. Static means of authenticating user access is no longer sufficient when it comes to identifying against advanced or ever-changing attack vectors such as theft of credentials, insider threats and hijacking of session. This study provides an overview of AI-enabled IAM implementations, showcasing how AI (machine learning, behavior biometrics, and predictive analytics, in particular) is transforming the identity security. Through the real-time detection of anomalies, the intelligent matchmaking of roles and policies, and continuous authentication, AI can turn IAM into proactive and adaptive security. The work discusses the structure and features of AI-enabled IAM, such as dynamic authorization, auto identity governance, and context-aware access control. It also discusses the ways that AI systems are learning and adapting based on user behavior and environmental context to deliver precise identity verification and granular access decisions. It also touches on deployment considerations like data fidelity, system integration and user buy-in, and highlights strategic benefits including reducing administrative workload, improving compliance, and enabling zero trust security models. The paper also suggests that IAM integrated with AI is a fundamental enabler of today's cybersecurity and a building block for scalable and intelligent digital transformation.