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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.
NHIMG editorial — based on content published by Trusona: Blog Your AI Agents Have Credentials. Nobody Verified Who They Are
By the numbers:
- A 2026 Gravitee survey found that 88 percent of organisations confirmed or suspected an AI agent security incident in the prior year.
- Rubrik Zero Labs puts the ratio of non-human to human identities at roughly 45 to 1 in a typical enterprise.
- Entro Labs has measured the non-human to human identity ratio at 144 to 1 in cloud and DevOps environments.
Questions worth separating out
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 AI agents complicate fraud detection and identity risk scoring?
A: Because they can imitate legitimate interaction patterns without being legitimate actors.
Q: How do you know if agent identity controls are actually working?
A: Look for whether you can reconstruct a complete path from trigger to identity to permission to action.
Practitioner guidance
- Define a unique identity for every AI agent Do not let multiple agents authenticate as the same account or token.
- Move sensitive actions behind pre-action verification Require an authoritative check before account resets, money movement, data export, or cloud provisioning completes.
- Separate credential lifecycle from actor verification Track where secrets live, who owns them, and how they rotate, but do not confuse that with proving the caller is legitimate at runtime.
What's in the full article
Trusona's full blog covers the operational detail this post intentionally leaves for the source:
- How Identity Impersonation Detection extends into autonomous systems and why that changes the verification model
- The specific agent verification flow described for sensitive actions before completion
- Implementation-oriented framing for API and SDK integration without relying on post-event log review
- The product-facing explanation of latency and setup expectations for practitioner planning
👉 Read Trusona's analysis of AI agent impersonation and identity verification →
AI agent impersonation: are your controls verifying the actor?
Explore further
25/08/2026 6:11 pm
AI agent identity is becoming a runtime verification problem, not just a governance problem. Discovery, ownership, and rotation are still necessary, but they only describe the credential estate. They do not prove that the entity acting at the moment of access is the one you authorised. Identity programmes that stop at lifecycle controls will miss the impersonation risk that now sits inside agentic workflows.
A few things that frame the scale:
- Only about 22 percent of organisations treat agents as distinct identity-bearing entities, while the rest rely on shared credentials, according to The State of Non-Human Identity Security.
- A separate finding shows that 85 percent of organisations lack full visibility into third-party vendors connected via OAuth apps, which is a familiar warning sign for delegated access sprawl.
A question worth separating out:
Q: Who should be accountable for AI agent shopping risk?
A: Accountability should sit across identity, fraud, and commerce risk, but one team must own the policy. Agentic shopping crosses traditional boundaries, so unclear ownership creates gaps in approval logic, telemetry, and incident response. The right model is shared execution with named accountability for delegation rules, exception handling, and customer-impact decisions.
👉 Read our full editorial: AI agents inherit the impersonation problem IAM never solved
