Author: Apurva Davé

The concept of nonhuman identity is gaining traction fast, sparking new debate over how it differs from managing service accounts.
AI agents are no longer just chatbots. They’re executing multistep workflows across tools and data sources, and the Model Context Protocol (MCP) standardizes these interactions.
Zero trust has reshaped how organizations secure user access. Multifactor authentication, single sign-on and continuous posture checks are now standard for human identities. But the same rigor rarely extends to the nonhuman side of the house.
When your team stores API keys in a vault and rotates them on a schedule, it feels like the access problem is handled.
For years, artificial intelligence has been reactive. You prompted it, and it responded by analyzing data, generating text or predicting outcomes, but only when asked.
Most workload credentials, the API keys, tokens and passwords that connect your services, carry “always on” access that never expires.
AI agent identity breaks down when agents authenticate across OAuth, API keys and managed identities simultaneously. Learn why single-protocol solutions fail.
While companies pour resources into securing employee accounts with MFA, zero trust and regular access reviews, service accounts still get created with static credentials, granted sweeping permissions and then left unmanaged. This creates a growing population of identities that operate outside traditional IAM controls.
The Trivy incident exposed a credential architecture failure, not just a supply chain one. Here’s the case for workload identity and access.
AI agent identity security is the set of practices and controls that treat AI agents as distinct, governable identities with their own authentication, authorization and audit requirements.