RBAC vs ReBAC for AI Agents: Best Authorization Model for Secure Agentic Systems

AI agent authorization requires a nuanced approach beyond traditional role-based access control (RBAC), incorporating relationship-based access control (ReBAC) for precision and adaptability in dynamic environments. While RBAC provides foundational guardrails by defining broad action categories agents can perform, it falls short in scenarios where real-time, context-specific decisions are crucial. ReBAC addresses these gaps by leveraging relationship modeling to determine if an agent can execute a specific action on a particular resource for a designated tenant. This approach is vital for ensuring secure operations as AI agents often operate under delegated authorities, interacting with multiple tools and resources within defined scopes. The combination of RBAC and ReBAC, supplemented by attribute-based access control (ABAC) or policy-based access control (PBAC) conditions, forms a comprehensive authorization framework, ensuring granular control and minimizing risks of over-permissioning. The implementation of such a system is critical for managing AI agents in production, where policy enforcement must be centralized and adaptable to evolving authorization demands.