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Why are AI agents putting pressure on legacy IAM?
Traditional IAM often assumes that an identity belongs to a person whose role changes occasionally and whose actions can be reviewed at a human pace. An agent may instead act for a signed-in person, run independently, or use a workload or service identity. It can also trigger many authorization and credential events quickly, while its permissions may be inherited from an existing account.
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That creates an operational problem: teams need to know which agents exist, who is accountable for each one, what each may do, and how to stop it. If agents are created outside an established process or retain access after their original task ends, ordinary account inventories and review cycles may not give teams a reliable picture.
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What identity patterns do agents use?
“AI agent identity” does not describe one universal implementation. The identity pattern determines whether actions can be attributed to an agent, a person, or both—and which permissions the agent may inherit.
| Pattern | How it works | Identity-management implication |
|---|---|---|
| Interactive or delegated agent | The agent acts for a signed-in user using delegated permissions. | Preserve the user context in authorization and logs, and make sure revoking delegation takes effect. |
| Autonomous agent | The agent runs independently and authenticates with its own identity. | Assign an accountable owner, grant narrow permissions, manage its lifecycle, and attribute logs to the agent. |
| Workload identity | The agent authenticates as a workload rather than as a person. | Manage it as a distinct non-human identity and connect its access to an owner and purpose. |
| Shared service account | An agent uses an account that may also be used by other software or actors. | Shared credentials can obscure which actor performed an action and may carry broader access than the agent needs. |
| Human identity | The agent operates under a person’s identity. | Actions can be difficult to distinguish from the person’s own activity, complicating review and accountability. |
These patterns are not necessarily mutually exclusive across an organization. An agent may use different identities in different environments, and an organization may use more than one pattern overall. The important design choice is to make the pattern explicit rather than silently relying on a broad shared or human identity.
What do the surveys say about current identity-management gaps?
Governance and credential lifecycle
In a Cloud Security Alliance (CSA) survey commissioned by Oasis Security, 383 IT and security professionals responded to an online survey conducted in August and September 2025. In the resulting 2026 report, 78% said their organization lacked documented, formally adopted policies for creating or removing AI identities, and 92% were not confident that legacy IAM solutions could effectively manage AI and non-human identity risks.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The same survey found that 14% of respondents said creation and removal of AI-related identities were fully automated, while 27% said the process was handled entirely by hand. More than 16% said they did not track when new AI-related identities were created. On credential response, 24% said it took more than 24 hours to rotate or revoke a credential after potential exposure. These findings point to gaps in process and visibility; they do not measure breach rates.
Attribution and access scope
A separate CSA survey commissioned by Aembit collected online responses from 228 IT and security professionals in January 2026. Published in March 2026, it found that 68% could not clearly distinguish AI-agent activity from human activity. In that survey, 74% said agents often received more access than necessary, and 79% believed agents created new access pathways that were difficult to monitor.
Respondents in the Aembit-commissioned survey reported using workload identities for agents (52%), shared service accounts (43%), and human identities (31%). The categories may overlap, so they should not be added together as if they describe mutually exclusive choices. Both CSA surveys were vendor-commissioned surveys; their percentages describe respondents’ answers, not independently measured incident rates or proof that one identity pattern caused an incident.
Can legacy IAM manage AI agents?
It can remain part of an organization’s approach, but its existing processes may need to be extended. The relevant question is not simply whether an IAM system supports an agent-related feature. It is whether the organization can consistently identify agents, assign responsibility, control what they can access, manage credentials throughout their life, and investigate their actions.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHillary Baron, CSA’s AVP of Research, said of the March 2026 survey: “The survey data indicates that existing IAM approaches were not designed for autonomous agents and are showing strain as deployments scale.” That is an attributed description of the survey findings—not a claim that every legacy IAM system is categorically unusable.
When assessing whether current controls are sufficient, check whether teams can:
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- Find agents and related identities across approved and less-visible deployments.
- Identify the person or team accountable for each agent and its access.
- Distinguish delegated user actions from an agent’s independent actions.
- Limit access to the resources and operations required for a specific task.
- Review, expire, rotate, and revoke credentials in a timely, observable way.
- Attribute activity to the agent and, where applicable, to its owner and delegating user.
- Withdraw access or contain execution when activity is suspicious.
How should organizations manage agent identities?
Use a lifecycle process that covers both identity and behavior. An inventory without ownership, or an access grant without an expiry and revocation path, leaves important gaps.
- Discover and inventory. Find agents, integrations, service accounts, tokens, owners, and environments. Include deployments that were created informally and agents that may have outlived their original purpose.
- Assign accountable owners. Record who approves each agent, who is responsible for its workload, and who handles access reviews and retirement. Ownership should be clear enough that someone can make a decision when access must change.
- Choose and document an identity pattern. Decide whether the agent acts with delegated user permissions or operates autonomously. Record when it uses a workload identity, service account, or human identity, and why.
- Constrain authorization. Grant only the permissions required for the task and resource. Review inherited permissions and pay particular attention to high-impact actions; avoid treating broad access as a convenient default.
- Automate credential lifecycle controls. Make credential creation, rotation, expiry, and revocation visible. Define what happens after suspected exposure, including who can act and how the credential or token is withdrawn.
- Retain attributable audit evidence. Log agent actions under the agent identity and preserve the delegating user and task context when relevant. Logs should help an investigator determine what happened without having to infer which agent used a shared credential.
- Test containment. Confirm that teams know how to disable an identity, revoke a token, or stop the agent runtime. A policy update alone may not be sufficient if execution or an active credential remains in place.
CSA’s March survey reported that respondents used actions such as disabling identities, revoking tokens, and stopping agent runtimes for containment; real-time policy changes were less common. That survey finding describes reported practice, not a guarantee that a particular containment method will work in every environment. Organizations should test their own paths.
What should teams look for in controls and tools?
Assess the control outcome rather than assuming that an “agent identity” label proves adequate protection. The useful comparison is whether each approach addresses the same operational requirements:
| Evaluation area | Question to ask |
|---|---|
| Identity attribution | Can activity be attributed to the agent, its accountable owner, and a delegating person when relevant? |
| Permission scope | Can access be limited by task, resource, and context, using a distinct identity instead of a broad shared credential? |
| Lifecycle coverage | Can teams discover, approve, provision, rotate, review, revoke, and retire identities consistently? |
| Runtime response | Can access be withdrawn or agent execution contained promptly after suspicious behavior or credential exposure? |
| Auditability and integration | Do logs support investigation and fit existing identity, security, and compliance workflows? |
These questions apply whether an organization extends existing IAM processes, uses workload identity controls, or adds non-human identity governance capabilities. A tool can support a control, but it does not replace decisions about ownership, least privilege, review, and incident response.
Is there a standard for AI agent identity?
NIST’s February 5, 2026 announcement introduced a concept paper on the identity and authority of software agents and described a potential National Cybersecurity Center of Excellence project. It solicited input on how organizations use or plan to use agents, what new problems agents create compared with other software, and what current or emerging standards guide agent identity and access management.
NIST’s May 18, 2026 summary analyzed responses to its request for information; it is not a completed agent-specific identity standard. Its summary says that “while fundamental cybersecurity principles and practices remain relevant, they will require adaptation to satisfactorily address agent security.” That is NIST’s account of broad agreement among commenters, not a statement attributed to a single named respondent.
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For now, organizations should treat agent identity as an evolving area of guidance and apply established security principles with explicit attention to software actors, delegated authority, lifecycle management, auditing, and non-repudiation.
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