September 9, 2026
Giving an AI agent access through an employee account might look like the pragmatic choice. The account is already authenticated, it already carries the right permissions, and the project can move without another lengthy security review. The problem surfaces the moment the agent starts to act. From that point, the line between the person and the software disappears. The audit log records that an employee opened a file, amended a record, or approved a transaction, when in fact an agent did the work. Accountability becomes guesswork, and the agent quietly holds far more access than its task requires.
The 2026 Gartner® Hype Cycle™ for Digital Identity defines the category this way: “AI agent identity is the unique digital representation of AI agents within an organization. Establishing and governing identity for AI agents, including assistants and chatbots, enables identity and access management (IAM) systems to assign unique identifiers, issue targeted credentials for access to resources (e.g., APIs, data, and services), and establish clear human accountability and audit trails across enterprise systems.”
That definition sets a clear bar, and most early AI deployments fall well short of it. The gap between having an agent and having an agent identity is where the risk sits.
Borrowed Credentials and Service Account Security Create a Blind Spot
Early AI projects tend to run on whatever access is already to hand. An internal assistant inherits the permissions of the employee using it. A workflow routes through a shared service account. A developer wires an agent to an API with a long-lived key. Each shortcut works, and each one introduces the same weaknesses. The agent often receives every permission the employee holds when it needs access to a single system. Human activity and agent activity blur together. Security teams may have no reliable way to know when an agent is running or which transactions it has completed.
This is why service account security has become an urgent problem rather than a housekeeping one. A shared service account was never designed to represent an autonomous actor making its own decisions at machine speed, and a static API key carries no information about who authorized the work or why.
Borrowed credentials also erode non-repudiation. When a person and an agent share one identity, the organization cannot prove which of them performed a given action. According to the report: “The hazardous practice of deploying AI agents as proxies operating with human access credentials breaks auditing, traceability, and nonrepudiation requirements, significantly elevating the risk of overpermissioning, and the impact of credential compromise and account takeover.“
What a Separate Identity Changes
A dedicated identity gives the agent its own record in the IAM environment, with a named owner, a defined purpose, and a deliberately narrow set of permissions. It can be switched off without disrupting the employee or team behind it. That separation makes real control possible. AI agent authentication becomes a distinct event you can see and reason about, rather than something hidden inside a human session. The agent can be issued short-lived credentials rather than a standing password or API key. Its access can be confined to a specific application, task, or transaction. Its activity can be reviewed on its own terms, apart from any human’s. Ownership can be reassigned as its role changes, and permissions can expire the moment the task ends.
This is much closer to how Zero Trust is meant to operate, where access reflects context and present need rather than the fact that an identity authenticated at some earlier point.
AI Agents Rarely Need Permanent Access
Human access usually follows a job role. Someone in finance needs ongoing access to a set of finance systems because their responsibilities persist. An agent rarely does. It may only need to pull a single invoice, match it against a purchase order, and return the result. Handing it the employee’s full permission set would be indefensible.
This is the case for ephemeral credentials. Where a human account holds a password for months, an agent can be issued a credential that lives for the length of one task and expires on completion. Nothing is left behind for an attacker to find.
Workload access management addresses this by replacing static credentials and standing permissions with dynamic, context-aware access. The report describes it as follows: “Workload access management, which is a part of an overall machine IAM program, secures the exploding universe of workloads, which includes AI agents, applications, containers and microservices, by enforcing least-privilege access at runtime, replacing static credentials with dynamic, context-aware controls. Workload access management eliminates standing permissions, reduces machine-to-machine attack surfaces, and closes the critical blind spot undermining most zero trust strategies.“
Identity Alone Doesn’t Explain Intent
A separate identity establishes which agent is acting. It says nothing about what that agent is trying to achieve. The distinction matters as soon as an agent receives a broad instruction such as “organize my business travel” or “resolve this customer’s account issue.” The agent may interpret the request in ways the user never intended, and it can be steered off course through prompt injection or poisoned data.
Authentication and authorization pull apart here. AI agent authorization has to answer a question that a sign-in cannot: is this specific action within the bounds of what the person actually asked for?
The report describes a further innovation profile addressing exactly this: “Intent-based access control is an emerging authorization framework that replaces broad, standing permissions and is currently targeted at agentic AI, but offers broad applicability. Intent-based access control grants access to back-end resources based on the captured or inferred intent of users interacting with an agent and evaluates the agent’s intended actions against that intent.“
Customer Agents Raise the Stakes
Organizations also need to prepare for agents they do not manage at all. Before long, customers will use their own agents to make purchases, update accounts, submit forms, and contact service teams. The business will have to establish who the customer is, whether the agent is genuinely authorized to act for them, and exactly what the customer has permitted it to do. The report points to “customer IAM solutions to manage AI agent identities in customer-facing contexts where customers send their AI agents to interact with organizations.“
We believe this is where the challenge sharpens. Detecting whether traffic came from a bot is not enough. Organizations need a trustworthy way to bind three things together: a real person, the agent acting on their behalf, and the specific permission that person has granted. That binding only holds if you can first prove a genuine human is present and is who they claim to be. Get that anchor right, and an agent’s authority traces back to a verified individual instead of a borrowed login.
We have looked at how that delegation should work in practice in a separate piece on verified human authorization in agentic AI.
Start by Separating People From Agents
Agents should not be invisible extensions of an employee’s account. Give each one its own identity, assign an accountable owner, keep its permissions tight, track what it does, and remove its access when the work is finished. Treated properly, this is AI agent lifecycle management rather than a one-time configuration: agents get provisioned, reviewed, reassigned, and retired, and AI agent governance is what keeps that cycle honest as the number of agents grows.
These steps ask for effort at the start of a project, and they head off a far larger problem later: thousands of autonomous agents operating through credentials that were only ever designed for people.
Gartner, Hype Cycle for Digital Identity, 2026, Zachary Smith, Nayara Sangiorgio, 6 July 2026.
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Next in the series: why identity visibility matters so much once humans, machines, and AI agents are all reaching into the same environment.
Frequently Asked Questions
Should AI agents have their own identity and permission system?
Yes. An agent that runs on a human account inherits every permission that person holds, and its actions are logged under their name. A separate identity lets you scope permissions to the task, assign an accountable owner, and review agent activity on its own terms.
What’s the risk of letting an AI agent use employee credentials?
Three risks compound. The agent is almost always over-privileged, because it receives the employee’s full permission set for a task that needs one system. The audit trail becomes unreliable, because human and agent actions are indistinguishable. And non-repudiation breaks, because you cannot prove which of the two performed a given action.
What is an AI agent identity?
It is a distinct record for an AI agent in your identity and access management environment, with its own identifier, credentials, owner, and permissions, separate from any human account. It exists so agent activity can be authorized, logged, and revoked independently of the people the agent works for.
How is non-human identity management different from traditional IAM?
Traditional IAM assumes a person with a job title, a manager, and stable responsibilities, so access is granted once and reviewed periodically. Non-human identities have no job role, can outnumber employees many times over, and may need access for seconds rather than years. That pushes the model toward short-lived credentials and runtime authorization instead of standing permissions.
How long should an AI agent’s access last?
As a rule, no longer than the task. Where a human account holds a credential for months, an agent can be issued one that expires when the work completes, which removes the standing permission an attacker would otherwise inherit.
