Abstract

The rapid exponential growth of Artificial Intelligence (AI), more so Large Language Models (LLMs), AI Agents, and Agentic AI, has ushered in revolutionary efficiencies and automation in business operations. As they become increasingly autonomous, smart, and rooted in workflows, however, they introduce a new wave of identity and access management (IAM) challenges. Traditional IAM controls, broadly designed to serve in large part static human identities, do not serve the behavior-based and dynamic nature of AI objects. This paper introduces an end-to-end, Zero Trust-based IAM system specifically for LLMs, AI agents, and agentic AI systems. The adopted model contains authentication mechanisms such as OAuth 2.0, mTLS, and TPM-bound tokens; ABAC and PBAC models based on AI-specific metadata (i.e., confidence values, model origin); and Just-in-Time privilege access mechanisms guarded by secrets vaults. Enterprise use cases modeled for the framework—payroll automation, document generation, CI/CD pipeline orchestration—underscore its significance. Metrics include a 75% reduction in credential exposure windows, 60% enhancement in audit traceability, and 40% enhancement in the effectiveness of anomaly detection. This effort addresses a critical void by putting IAM not as a bottleneck nor an inhibitor but as an underpinning facilitator to scale, secure integration of AI. The proposed architecture aligns with NIST AI Risk Management Framework, OWASP Agentic Threat recommendations, and CSA’s Zero Trust Maturity guidance. It also sets the stage for future agent identity schema standards, AI behavior policy declaration, and governance automation.

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