Designing Conditional Access policies for AI agent identities
How to apply Conditional Access to AI agent identities in Microsoft Entra: autonomous agents, approval-based targeting, and safe staged rollout guidance.
This installment in the Microsoft Entra Agent ID governance series explains how to convert agent inventory, ownership and custom security attribute data into enforceable Conditional Access policies. Rather than building one policy per agent, the recommended approach reuses classification metadata—approval status, access pattern, environment and data sensitivity—to drive access decisions. Agents are grouped into three access patterns: on-behalf-of-user agents, autonomous agents authenticating with their own identity, and user-like agent identities with persistent access, each requiring a distinct Conditional Access strategy.
For on-behalf-of agents, the guidance is to first check whether existing user-based Conditional Access policies (MFA, device compliance, location controls) already provide adequate coverage before creating anything new. Autonomous agents, which cannot complete interactive MFA or remediation prompts, should instead be targeted using custom security attributes such as approval status (Approved/Rejected/Revoked), risk level, environment (Prod/Test/Sandbox) and data sensitivity—with unknown or unapproved agents denied access by default.
On the rollout side, the article recommends validating policies in report-only mode and using a staged ring-based deployment, moving from pilot agents to broader production enforcement. A baseline policy set is proposed: blocking unapproved autonomous agents, allowing scoped access for approved ones, blocking high-risk agent identities, and applying stricter controls for sensitive or business-critical agents. For engineers, the key takeaway is that identity and access architectures can't simply reuse human-user policy patterns for AI agents—non-human identities require a distinct, signal-driven policy design approach.