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02/08/2026 11:38 am
TL;DR: Endpoint AI agents run at the OS layer on employee devices, bypassing browser, proxy, and many DLP controls, while Cyberhaven says enterprise adoption of endpoint-based AI-native apps grew 509% and coding assistants 357% year over year. Current governance models fail because they were built for network-visible workflows, not local, multi-step agent execution.
NHIMG editorial — based on content published by Cyberhaven: Endpoint AI Agents: The New Security Blind Spot
By the numbers:
- enterprise adoption of endpoint-based AI-native apps has grown 509% over the past year
- enterprise adoption of coding assistants has jumped 357% year over year
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do endpoint AI agents create a security blind spot for current controls?
A: Because they operate at the OS layer and can access data without generating the browser or proxy events most security tools rely on.
Q: What breaks when AI agent activity is monitored only through SIEM and DLP?
A: You miss the sequence that matters.
Practitioner guidance
- Deploy endpoint discovery for AI agents Inventory AI tools running on laptops, developer workstations, and local environments, then map which users, teams, and business processes depend on them.
- Extend data lineage to endpoint activity Track which files, credentials, and records an agent touches on the device, then preserve the chain of custody across subsequent API calls or application actions.
- Rebuild monitoring around workflow sequences Create detections that look for a sequence of file access, API usage, and outbound transmission rather than a single upload or paste event.
What's in the full article
Cyberhaven's full blog post covers the operational detail this analysis intentionally leaves for the source:
- The article details how Cyberhaven's AI Agentic Security discovers local AI apps and agents across laptops, developer workstations, and command-line environments.
- It explains how workflow-level understanding reconstructs multi-step data access and transmission across endpoint actions.
- It describes how data lineage is used to trace where endpoint data originated and where it moved after agent processing.
- It outlines the real-time guardrails used when agent behaviour crosses a defined threshold, including blocking, warning, and contextual guidance.
👉 Read Cyberhaven's analysis of endpoint AI agents and the security blind spot →
Endpoint AI agents: what IAM and security teams are missing?
Explore further
02/08/2026 12:51 pm
Endpoint AI agents are an endpoint visibility problem before they are an AI problem. These tools run where browser controls, proxy inspection, and many DLP policies were never designed to operate. That means security teams lose the normal observation points that make identity and data governance enforceable. The practitioner conclusion is simple: if the control plane stops at the browser, the endpoint becomes the blind spot.
A few things that frame the scale:
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to The State of Non-Human Identity Security.
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, which shows how quickly governance weakens once identity activity moves outside the primary control plane.
A question worth separating out:
Q: How can organisations reduce risk from shadow AI agents already inside the enterprise?
A: Organisations should combine continuous scanning, access reduction, and credential revalidation for any agent found outside formal governance. The priority is to move unknown agents into a managed state, then decide whether they are sanctioned, constrained, or removed. That sequence is more effective than waiting for a full platform redesign.
👉 Read our full editorial: Endpoint AI agents expose the blind spot in current governance models
