Authorization for AI Agents: Beyond RBAC. Capability Tokens, User-Context-Aware Policy, and the Limits of Roles

Mohamad Amin Hasbini

doi:10.5281/zenodo.20242386

April 28, 2026Open Access

Authorization for AI Agents: Beyond RBAC. Capability Tokens, User-Context-Aware Policy, and the Limits of Roles

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Key Points

The research aims to develop an advanced authorization framework for AI agents that surpasses traditional RBAC methods.
Developed the AAC framework focusing on Authenticate, Authorize, and Communicate components.
Evaluated authorization strategies in real-time against agent capabilities and user contexts.
Analyzed the limitations of existing role-based access control in dynamic environments.
Proposes a new model that enhances security by evaluating agent permissions in real-time.
Identifies key factors in user delegation affecting task authorizations.
Highlights the limitations of fixed roles in managing non-human identities effectively.

Implication

Evaluates real-time authorization for AI agents, suggesting improved security and efficiency.

Abstract

RBAC was built for humans in stable roles. Agents require authorization evaluated per-task, in real time, against the intersection of agent capability, user delegation, and task scope.Paper #2 of the Non-Human Identity Series. Develops the authorization pillar of the AAC (Authenticate / Authorize / Communicate) framework.

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Mohamad Amin Hasbini (2026) studied this question.

synapsesocial.com/papers/6a0aad015ba8ef6d83b7060a https://doi.org/10.5281/zenodo.20242386

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Authorization for AI Agents: Beyond RBAC. Capability Tokens, User-Context-Aware Policy, and the Limits of Roles

Key Points

Implication

Abstract

Discussion

Cite This Study

Authorization for AI Agents: Beyond RBAC. Capability Tokens, User-Context-Aware Policy, and the Limits of Roles

Key Points

Implication

Abstract

Discussion

Cite This Study

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