The rapid proliferation of goal-oriented, autonomous AI agents has fundamentally disrupted traditional identity management paradigms. In high-compliance sectors such as FinTech and Healthcare, AI agents are increasingly authorized to execute sensitive transactions and retrieve protected health information (PHI) with minimal human intervention. However, current security infrastructures lack a standardized “first-class identity” for these entities, relying instead on legacy patterns like Implicit Context Inheritance or Long-Lived Service Tokens. These anti-patterns violate the Principle of Least Privilege (PoLP), leading to unmanageable security impact scopes. This paper investigates the necessity of transitioning to a dynamic, identity-centric Attribute-Based Access Control (ABAC) model. Through a 12-month architectural audit of N > 100 agents, we demonstrate that a First-Class Identity framework effectively decouples agent authority from broad user sessions.
I am Dwijen Kirtania, an Engineering Leader in a top-tier Fintech organization with over 18 years of industry experience. As a seasoned programmer and architect, I’ve spent my career building high-stakes, high-availability systems where failure isn't an option. Beyond the keyboard, I am an IEEE Senior Member and a dedicated AI researcher with five published papers. While I am a passionate AI enthusiast and lifelong learner, my Fintech background drives me to look past the hype. I specialize in the architectural integrity of the AI supply chain, advocating for a shift from "check-box" governance to mathematically secured autonomous superagency. I’m here to bridge the gap between deep-stack engineering and the next generation of autonomous systems—ensuring we build for resilience, not just scale.
