Table of Contents
IAM for Agentic AI: Aembit's Approach to Closing the AI Identity Gap
TL;DR: AI agents often operate through shared API keys, user sessions, or no distinct identity at all. Aembit closes that gap with blended identity, policy evaluated at every request, and credential exchange. The result is agent access that remains attributable, revocable, and least-privileged across cloud, SaaS, and on-premises environments.
Aembit Team
Product & Research
Published Sep 2026
Updated Sep 2026
50:1
Non-human to human identities
18
Agent threat classes mapped
0
Long-lived secrets required
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Table of Contents
IAM for Agentic AI: Aembit's Approach to Closing the AI Identity Gap
When an AI agent borrows a human’s identity, accountability gets lost in the handoff. Shared API keys and full user sessions make it difficult to determine which agent acted, what authorized it, and whether its access matched the task.
This white paper lays out Aembit’s approach to closing that gap with an identity control plane built for agents and workloads.
Inside, You’ll Find:
- How blended identity preserves both user and agent attribution
- How runtime policy adjusts access for each request
- How credential exchange delivers short-lived access without storing secrets in the agent
The paper also recounts how a $300 billion investment firm applied this model to an enterprise Claude deployment, moving from start to production in two weeks with fewer than six hours of security team time.
FAQs
You have questions? We have answers.
What is IAM for agentic AI?
IAM for agentic AI applies identity and access controls to agents as they connect to APIs, data, applications, and other enterprise resources. It gives security teams a way to identify agents, authorize individual requests, limit access, and attribute each action.
Why do AI agents need their own identities?
Agents often operate through shared API keys, service accounts, or a user’s full session. These approaches obscure whether a person or an agent performed an action and can give the agent broader access than its task requires. A distinct agent identity restores that separation.
What is blended identity?
Blended identity combines the identity of an AI agent with the identity of the user it represents. Aembit can evaluate both identities when deciding whether to authorize a request, preserving attribution without allowing the agent to inherit the user’s access wholesale.
How does Aembit apply policy to AI agent access?
Aembit evaluates identity, policy, and available posture signals when each access request occurs. It determines whether the request should proceed, what level of access is appropriate, and which credential the destination requires.
How does Aembit keep credentials out of AI agents?
Aembit exchanges an approved access decision for the credential accepted by the destination system. The agent receives short-lived, scoped access without storing the destination credential itself.
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