The Problem with AI Agents Isn't Identity, It's Authorization (fusionauth.io)

A recent discussion emphasizes that the primary challenge with AI agents lies not in their identity but in the complexity of authorizing their access to systems and APIs. While establishing agent identity is important, the real hurdle is creating granular authorization frameworks that limit how much access these agents have. An overly broad authorization model can expose critical systems to risk, especially when agents interface with sensitive resources like bank accounts or cloud infrastructures. Current API authorization methods, such as Google Drive’s coarse-scoped permissions, fall short for AI agents that need more refined access control. Advancements like Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC) offer potential solutions. RBAC provides fundamental role definitions, while ABAC enables dynamic constraints based on context, and ReBAC models relationships for precise resource access. However, implementing these systems requires significant effort, including lifecycle management of authorization, dedicated consent interfaces, and audit tools to manage complexity effectively. Without addressing these issues, the authorization landscape for AI agents could lead to broader access patterns that are increasingly difficult to rectify as integrations scale.