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06/06/2026 1:45 am
TL;DR: Enterprises are deploying AI agents on both sides of the firewall, but workforce agents and customer agents create different identity risks, from internal blast radius to cross-tenant leakage, according to Aembit. The governance gap is not access alone, but the assumption that human IAM patterns can govern machine-speed delegation.
NHIMG editorial — based on content published by Aembit: Workforce and customer agents and how to secure them
By the numbers:
- Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
Questions worth separating out
Q: What breaks when AI agent access relies on long-lived secrets?
A: Long-lived secrets let AI agents carry persistent access far beyond the task they were created for.
Q: Why do customer AI agents complicate tenant isolation?
A: Customer agents often operate on behalf of a user while sharing infrastructure across many customers.
Q: What do security teams get wrong about AI agent identity governance?
A: They often assume human IAM patterns can be reused with minor adjustments.
Practitioner guidance
- Separate internal and external agent trust zones Define workforce and customer agent policies in different trust zones, even if they share the same platform.
- Replace static secrets with runtime attestation Use workload identity attestation and short-lived tokens for agent authentication so there are no long-lived credentials to rotate or steal.
- Bind delegated access to both agent and user identity For customer-facing workflows, issue credentials that are scoped to the user session and the specific tenant, not just to the agent instance.
What's in the full article
Aembit's full guide covers the operational detail this post intentionally leaves for the source:
- Step-by-step secretless authentication patterns for internal workforce agents running in Kubernetes and cloud environments.
- High-volume identity verification patterns for customer-facing agents that cannot tolerate central IAM bottlenecks.
- Blended identity and token exchange design for delegated customer workflows.
- Operational examples for revoking agent access instantly when posture, session state, or business context changes.
👉 Read Aembit's guide to workforce and customer AI agent identity →
Workforce vs customer AI agents: where IAM controls diverge?
Explore further
06/06/2026 3:02 am
Workforce agents and customer agents are both non-human identities, but they fail differently. Workforce agents concentrate risk inside the enterprise blast radius, while customer agents concentrate risk at the tenant boundary. That distinction matters because identity controls that are sufficient for internal automation can still fail when the same access model is extended to external users. Practitioners should treat the deployment boundary as part of the governance boundary.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an AI agent accesses the wrong data?
A: Accountability sits with the team that defined the agent’s scope, the owner of the delegated user context, and the operators who allowed access to persist beyond the task. For customer workflows, audit logs should show both the agent and the user identity so responsibility can be traced clearly.
👉 Read our full editorial: Workforce and customer AI agent identity needs different controls
