LLM-ENHANCED ZERO TRUST SECURITY MODELS: AI/ML-DRIVEN IDENTITY AND ACCESS MANAGEMENT

Abstract

This will be especially huge in a world where cybersecurity is constantly changing. Zero Trust Security Models have emerged as a robust framework that will go a long way in mitigating the risks associated with unauthorized access and data breaches. The concept of Zero Trust depends on IAM’s cornerstone: ensuring that only authenticated and authorized users are able to access critical resources. This paper will discuss the integration of LLMs and sophisticated AI/ML technologies for improving IAM in Zero Trust Architecture. Based on the proposed model, taking leverage from the massive capabilities of LLMs in understanding natural language and conducting contextual analysis, better facilitation for user authentication use cases, dynamic access control, and anomaly detection shall be achieved. AI/ML algorithms provide more strength to these by giving predictive analytics and real-time threat intelligence. This synergy, besides truly bringing in better precision for identity verification processes, can adapt much quicker to an ever-changing threat landscape. The research portrays, through theoretical frameworks backed by practical implementations, how LLM-enhanced Zero Trust models can desirably surge an organization’s security posture, reduce insider threat risks, and facilitate smooth, secure access to digital assets. The results have brought into perspective the possibility of integrating advanced language models into cybersecurity strategies that will open up ways to more intelligent and resilient defense mechanisms against continuous evolutions of cyber threats.