For years, enterprises have built governance processes around a simple assumption: privileged access belongs to people.
Administrators receive elevated permissions. Developers receive access to production environments. Finance employees receive access to financial systems. Security teams identify that access, and auditors expect organizations to periodically verify that it is still appropriate.
AI is changing that model.
A recent Dark Reading article, “AI Agents Are Privileged Users; Who Is Auditing Their Access?”, makes an important point: autonomous AI agents are increasingly being granted access to cloud infrastructure, databases, repositories and other sensitive enterprise resources. In many cases, they operate through service accounts, API tokens or delegated permissions.
Yet those identities may not receive the same governance scrutiny as human privileged users.
Source: Dark Reading — AI Agents Are Privileged Users; Who Is Auditing Their Access?
When an AI agent takes an action inside an enterprise, something has authorized that action.
AI Has an Identity — And That Identity Has Privileges
- A dedicated service account
- An API credential
- A workload identity
- A delegated cloud permission
- Or an existing human identity and its authenticated session
That last category is particularly important.
Tools such as Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot and Cursor can operate in the context of an existing user. Depending on configuration, the agent may therefore be able to interact with resources available to that employee.
Other AI agents operate using dedicated NHIs.
From an identity governance perspective, both models create the same fundamental question:
What can the identity behind the AI access — and has anyone recently verified that it should still have that access?
The Problem Isn’t AI. It’s Unreviewed Authority.
Dark Reading describes a useful example: imagine an AI automation agent running under a service account with broad AWS permissions.
The agent’s original purpose may be perfectly legitimate. Perhaps it monitors infrastructure and makes automated changes to improve availability.
But what if the underlying identity has accumulated far more authority than the agent actually requires?
The AI may now have the technical ability to modify infrastructure, assume additional roles, access storage or perform actions nobody intended when the agent was originally deployed. Dark Reading notes that an autonomous system could potentially become the initiator, approver and executor of a process—undermining traditional segregation-of-duties controls.
That is an identity governance problem.
The agent doesn’t need to “break into” anything.
The identity already has permission.
A Practical Enterprise Use Case
Consider a software developer using an AI coding agent.
The developer has accumulated access over several years:
- Production GitHub repositories.
- AWS development and production environments.
- Internal file shares.
- A customer database used for troubleshooting.
- Several applications inherited from a previous role.
Some of that access is legitimate. Some of it may no longer be necessary.
Now introduce an AI coding agent operating within that developer’s environment.
The security question isn’t simply whether the AI tool itself is safe.
The more immediate question is:
What authority have we made available to it through the identity it is using?
The same applies when an AI agent has its own NHI. A service account created for an AI project six months ago may still exist today with privileges nobody has revisited.
The common vulnerability is unreviewed access.
Every Identity With Authority Needs Governance
This creates an important shift for Identity Governance.
Enterprises traditionally review employees, contractors, administrators and other human users.
That scope now needs to include the identities being used by automation and AI.
For every identity with meaningful enterprise access, organizations should be able to answer:
- Who or what is this identity?
- Is it human or non-human?
- Who owns it?
- What resources can it access?
- What privileged roles does it possess?
- Why does it need those privileges?
- When was that access last reviewed?
- And does it still need that access today?
Dark Reading argues that autonomous identities should undergo regular access certification, warning that without entitlement reviews they can persist after projects or platforms have been abandoned.
That is exactly where traditional Identity Governance becomes extremely relevant to AI security.
Start With a User Access Review
Organizations don’t need to wait for an entirely new category of AI security technology to address this risk.
They can start with something they already understand:
Review the access.
YouAttest automates User Access Reviews (UARs) so organizations can review and certify the entitlements associated with both human and non-human identities.
For human accounts—including employees whose authenticated environments may be used by tools such as Claude Code, Codex or Gemini CLI—the organization can review existing application, group, role and resource access and remove privileges that are no longer required.
For non-human identities, including service accounts, automation accounts and AI agents, organizations can identify ownership and review whether the identity and its privileges are still necessary.
The technology behind the identity may be changing rapidly.
The governance question hasn’t changed:
Does this identity still need this access?
If the answer hasn’t been reviewed recently, now is a good time to find out.
Learn more about YouAttest User Access Reviews: https://youattest.com
- AI Agents Are Privileged Identities. Their Access Needs to Be Reviewed. - October 3, 2026
- AI Is the Unrealized Insider Threat: Why Enterprise AI Security Starts With Existing User Access - September 20, 2026
- The Service Account Wasn’t the Initial Breach. It Became Part of the Attack. - September 13, 2026
