Topic starter
25/08/2026 6:10 pm
TL;DR: AI agents inherit the same impersonation risk that human identity controls were built to stop, but they act at machine speed and often reuse shared credentials, according to Trusona. Governance around discovery and rotation is necessary, yet the unresolved problem is verifying which agent is actually acting at the moment of access.
Editorial analysis by NHI Mgmt Group, based on content published by Trusona: “Your AI Agents Have Credentials. Nobody Verified Who They Are.”.
By the numbers:
- The ratio of non-human to human identities is roughly 45 to 1 in a typical enterprise.
- The ratio of non-human to human identities is closer to 144 to 1 in cloud and DevOps environments.
- A 2026 Gravitee survey found that 88% of organisations confirmed or suspected an AI agent security incident in the prior year.
Key questions
Q: What breaks when AI agents are connected through personal accounts or shared credentials?
A: Shared or personal credentials break accountability, lifecycle control, and revocation.
Q: Why do agent credentials create more risk than traditional IAM models expect?
A: Traditional IAM models assume the identity behind an action is stable enough to inspect, review, or challenge.
Q: What are the signs that identity governance is failing across SaaS and AI agent environments?
A: Identity governance is failing when local accounts, service accounts, API keys, and OAuth grants exist outside the directory, especially if they have no owner, no expiry, or survive offboarding.
Practitioner guidance
- Separate agent identities by runtime actor Stop allowing multiple AI agents to authenticate as a single shared identity when the workflow can move money, reset accounts, or touch privileged systems.
- Bind sensitive actions to authoritative verification Require the system to confirm the actor against an authoritative source before the request completes, especially for high-impact actions that previously relied only on credential possession.
- Review where governance stops before action time Map every place your programme discovers, scopes, and rotates secrets, then identify where those controls do not verify the current agent at the moment of use.
Bottom line: AI agents inherit an old impersonation problem, but shared or unattended credentials make it harder to separate authorised automation from a spoofed actor.
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25/08/2026 6:11 pm
Agent impersonation is the control gap that NHI governance alone does not close: ownership, rotation, and scoping are necessary, but they do not prove which runtime actor is actually taking a sensitive action. The article is right to separate hygiene from verification because agentic workflows can present valid credentials while being the wrong actor. The implication is that identity programmes must treat actor assurance as a separate control domain, not a stronger version of credential management.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should security teams respond when agent impersonation is a real concern?
A: They should move beyond discovery and rotation to runtime actor verification for sensitive actions. That means each agent needs a distinct identity, and the system should check the agent against an authoritative source before approval completes. Without that step, shared credentials can make legitimate automation and impersonation look the same.
👉 Read our full editorial: AI agents inherit the impersonation problem IAM never solved