Topic starter
10/07/2026 9:08 pm
TL;DR: AI agents now create a new lateral movement path because they hold broad authenticated access across email, databases, code repositories, cloud APIs, and internal services, according to Zero Networks. The real problem is not just alerting, but the collapse of human-paced boundary reviews when agents can combine legitimate permissions into unexpected trust bridges.
Editorial analysis by NHI Mgmt Group, based on content published by Zero Networks: “AI Agent Security: Real-World Attack Techniques (and How to Stop Them)”.
By the numbers:
- Nearly 80% of organisations are now deploying AI agents, according to Zero Networks.
Key questions
Q: What breaks when an AI agent can act across multiple business systems?
A: Traditional helpdesk controls break because they assume a human can be held at the centre of the workflow.
Q: Why do AI runtime credentials increase the risk of lateral movement?
A: Runtime credentials turn a model from a controlled evaluator into an actor that can reuse access across systems.
Q: What signs show that an AI agent is crossing trust boundaries?
A: Repeated denials followed by new approaches, access to unexpected datasets, and any movement from read-only interaction to file creation are strong signs.
Practitioner guidance
- Map the full agent access graph Inventory every system an agent can read, write, search, or invoke, then identify where those permissions create cross-system adjacency that was never designed into the architecture.
- Separate untrusted content from control inputs Treat email bodies, ticket text, web pages, and metadata as potential instruction carriers and block them from directly steering privileged actions unless the workflow is explicitly governed.
- Remove toxic permission combinations Review agent entitlements as a set and eliminate combinations such as read plus write plus search across unrelated systems when those privileges create a trust bridge.
Bottom line: AI agents change lateral movement from a stolen-credential problem into a permission-combination problem, because their normal tool access can bridge systems that were never designed to trust each other.
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10/07/2026 9:31 pm
AI agent lateral movement is an identity graph problem, not just an endpoint problem. The article shows that the meaningful security boundary is the combination of authenticated tool reach, not the individual agent or the individual system. When one identity can move through email, tickets, repositories, and APIs, the attack surface is the adjacency between those systems. Practitioners need to reason about how the access graph changes when an agent can chain legitimate actions across multiple services.
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: How should teams govern AI agent traffic without blocking legitimate automation?
A: By separating delegated agent sessions from unauthorised automation and setting different rules for read-only crawling, structured interaction, and transactional access. Governance should be based on intent and permitted action scope, not on whether traffic is automated. That keeps useful agent activity from being treated as generic bot noise.
👉 Read our full editorial: AI agent lateral movement exposes identity control gaps