Table of Contents
IAM for Agentic AI: Aembit’s Approach to Closing the AI Identity Gap
TL;DR: Turn agent access from an inherited assumption into an enforceable decision. This white paper maps the identity models behind workforce, consumer, and autonomous agents, then shows how to verify each request, issue scoped credentials, preserve attribution, and extend the same policy model across cloud, SaaS, and on-premises systems.
Aembit Team
Product & Research
Published Aug 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
When AI agents start taking real action inside enterprise systems – calling APIs, accessing data, chaining tools – the question of what they’re allowed to do becomes tantamount. This graphic maps the full risk surface so security teams and builders can see it clearly, all at once.
Every known agent threat – 18 of them across five domains – maps into a single tree, with tiers that escalate by blast radius and one root that connects them all: the agent’s identity.
Inside, you will find:
- A tree of known agent threats, organized by domain and blast radius.
- Five risk domains spanning identity confusion, instruction manipulation, supply chain, runtime execution, and persistence.
- A tiered model showing how threat severity escalates from initial influence to lasting compromise.
- A clear approach to reducing risk through verified, scoped, short-lived credentials.
- Key data points on how quickly organizations are deploying agents and where critical controls remain absent.
FAQs
You Have Questions? We Have Answers.
How should organizations begin securing AI agent access?
Start by mapping who each agent acts for, where it runs, and which systems it can reach. That inventory exposes where agents rely on shared credentials, inherited user access, or incomplete identity signals, and it gives security teams a practical basis for choosing the right controls.
How can security teams close the AI identity gap?
Assign each agent a distinct identity, bind it to the user it represents when required, and evaluate those signals together before access is granted. Replace broad, persistent credentials with short-lived, policy-scoped access, and record every decision for investigation, audit, and revocation.
What does "blended Identity" allow security teams to control?
Blended identity lets security teams evaluate the agent and the person behind the request as part of one policy decision. That makes it possible to narrow access by user, agent, resource, task, and runtime context instead of allowing the agent to inherit the user’s full permissions.
How does Aembit govern and enforce AI agent access at runtime?
Aembit verifies the agent, incorporates relevant user and posture signals, applies policy when the request occurs, and brokers the credential the destination accepts. This removes stored secrets from the agent, constrains access to the approved interaction, and leaves a complete record of the decision.
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