Topic starter
11/07/2026 8:24 pm
TL;DR: Enterprises are deploying AI agents directly into servers, VMs, and Kubernetes clusters, creating exposed APIs, machine identities, and lateral movement paths that traditional ZTNA was not built to secure, according to Appgate. The governing assumption is collapsing because policy-only guardrails do not stop autonomous workloads that run inside core infrastructure and outside browser-centric access models.
Editorial analysis by NHI Mgmt Group, based on content published by Appgate: “Like Ashtrays on Airplanes: Why Every Company Must Secure AI Agents—Even If They Ban Them”.
Key questions
Q: What breaks when AI agents are given broad enterprise access without tight governance?
A: Broad access turns AI agents into high-speed execution paths that can move data, spend money, modify records, or delete assets before operators can intervene.
Q: When do AI agent credentials create more risk than they reduce?
A: They create more risk when they are long-lived, over-scoped, hard to revoke, or copied into code and prompts.
Q: How can security teams tell whether zero trust is actually covering autonomous workloads?
A: The clearest sign is whether services remain reachable before identity is verified and authorised.
Practitioner guidance
- Map AI agents to machine identities Inventory every agent that runs in servers, VMs, and Kubernetes clusters, then record the service identities, tokens, and APIs it can reach.
- Enforce workload-layer zero trust Move enforcement to the point of execution so the agent is authenticated and authorised before it can reach internal services or ports.
- Reduce east-west reachability Limit internal service-to-service paths so an agent cannot use one trusted connection to discover or pivot into adjacent systems.
Bottom line: AI agents running inside enterprise infrastructure change the problem from human access management to workload identity governance.
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11/07/2026 8:54 pm
AI agents inside the enterprise core expose an identity governance gap, not just a network design gap. The article shows workloads running where traditional ZTNA was built to stop at the edge, while the identity they use is machine-bound rather than human-bound. That means the security question is no longer only who is logging in, but what is executing with autonomous reach inside the core. Practitioners should treat agent runtime identity as a first-class governance object.
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.
A question worth separating out:
Q: Should organisations govern AI agents differently from chatbots?
A: Yes. Chatbots mainly expose content risk, while AI agents create action risk because they can decide, select tools, and execute in live systems. Governance has to focus on delegated authority, business-process containment, and runtime boundaries rather than conversation safety alone.
👉 Read our full editorial: AI agent identity needs zero trust inside the enterprise core