The Convergence of IAM and AI: How Large Language Models Are Reshaping Cybersecurity

Abstract

The convergence of Identity and Access Management (IAM) and Artificial Intelligence (AI), mainly through Large Language Models (LLMs), represents a transformative shift in cybersecurity paradigms. This article explores how LLMs reshape identity security across multiple dimensions, enabling more sophisticated defense mechanisms against evolving threats. Traditional static IAM frameworks give way to dynamic, contextual systems capable of continuous evaluation and adaptive response. The integration of natural language processing enhances authentication through linguistic analysis and behavioral pattern recognition, while contextual access control architectures implement zero-trust principles with unprecedented granularity. LLM capabilities further enable autonomous policy generation and management, creating living security frameworks that evolve alongside threat landscapes. Predictive analytics capabilities shift organizational security postures from reactive to anticipatory, identifying attack precursors before exploitation. Despite significant implementation challenges, including computational requirements, potential vulnerabilities, and governance considerations, the strategic integration of LLMs with IAM systems promises to fundamentally transform cybersecurity from discrete tool collections into unified intelligent ecosystems. This technological convergence creates multidimensional security capabilities that adapt continuously to changing conditions, representing an incremental improvement and a fundamental rethinking of identity-centered security.