Authenticated Delegation and Authorized AI Agents

• URL: http://arxiv.org/abs/2501.09674v1
• Date: Thu, 16 Jan 2025 17:11:21 GMT
• Title: Authenticated Delegation and Authorized AI Agents
• Authors: Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Dazza Greenwood, Alan Chan, Alex Pentland,
• Abstract summary: We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents.<n>We propose a framework for translating flexible, natural language permissions into auditable access control configurations.
• Score: 4.679384754914167
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know whom AI agents act on behalf of and guide their use appropriately, protecting online spaces while unlocking the value of task delegation to autonomous agents. We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents, where human users can securely delegate and restrict the permissions and scope of agents while maintaining clear chains of accountability. This framework builds on existing identification and access management protocols, extending OAuth 2.0 and OpenID Connect with agent-specific credentials and metadata, maintaining compatibility with established authentication and web infrastructure. Further, we propose a framework for translating flexible, natural language permissions into auditable access control configurations, enabling robust scoping of AI agent capabilities across diverse interaction modalities. Taken together, this practical approach facilitates immediate deployment of AI agents while addressing key security and accountability concerns, working toward ensuring agentic AI systems perform only appropriate actions and providing a tool for digital service providers to enable AI agent interactions without risking harm from scalable interaction.

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