Authorization of Agents
Open Until: 11/13/2026
Artificial Intelligence (AI) agents are becoming part of everyday operations in organizations. AI Agents can perform tasks, access applications, fetch information, and most importantly make decisions based on their intelligence (with minimal human involvement). As organizations begin to rely on these agents, it is essential to control what they can do and what they cannot do. Traditional authorization methods were designed for human users but very limited to non-human users like service accounts, bots etc. AI agents introduce new challenges because these agents can work independently, interact with systems & applications, and even act on behalf of users. Without proper security controls and guardrails, AI agents may be given more access than they need, which increases the risk of unauthorized actions, data exposure, and misuse of organizational resources. This paper explains why authorization of AI agents requires a different approach. It covers the key challenges, reviews various authorization models, discusses secure agent to agent interactions, and explores how organizations can build authorization frameworks that are secure and ready for future.
Key Takeaways
- Catalog every agent and assign an accountable owner. You can't govern or audit agents you don't know about.
- Give each agent its own short-lived identity and confirm delegation reduces scope. A token naming both the user and the agent can still carry the user's full access.
- Require human approval for actions that can't be undone. A chain of actions is only as safe as its riskiest step.
