Table of Contents
A Day in the Life: Deploying AI Agents
TL;DR: Alex faces three AI agent access problems in one workday: persistent credentials at deployment, uncertain attribution during an audit, and delayed approval for the next rollout. See how blended identities, runtime access policy, and complete access records give them a clearer answer at each stage.
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.
What happens when AI agents inherit a user’s full access?
The distinction between the user and the agent becomes difficult to preserve. An agent may receive the same identity and permissions as the user who created it, which makes access harder to limit and individual actions harder to attribute.
How does Aembit secure AI agent access during deployment?
Aembit gives each agent a blended identity tied to both the agent and the user. Access policy is defined before deployment, while credentials are issued at runtime, restricted to the task, and allowed to expire when the session ends.
Why can AI agent audits become difficult?
Shared credentials and service accounts often fail to distinguish among agents, users, and individual sessions. Security and compliance teams may then have to reconstruct activity from separate server logs without a dependable record of which agent accessed which resource.
What information should an AI agent audit record contain?
An AI agent audit record should identify the agent, the resource it accessed, the associated user session, the policy that authorized the request, and the time of access. This gives security and compliance teams a direct account of the event.
How can identity controls shorten approval for new AI agents?
Security teams can review the proposed access policy before deployment and confirm that the agent will not receive persistent access. That evidence can reduce delays caused by retrospective documentation and unresolved credential questions.
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