Topic starter
23/10/2026 12:45 am
TL;DR: Different asset counts across tools are Forescout's evidence that agentic security decisions can rest on partial truth. Visibility across IT, OT, IoT and IoMT determines whether autonomous response can contain the right asset without disrupting the wrong one.
Editorial analysis by NHI Mgmt Group, based on content published by Forescout: “The Need for Smarter Network Security in the AI-Driven Era”.
Key questions
Q: Why does incomplete visibility increase risk in agentic security?
A: Because automation compounds the quality of the input it receives.
Q: How do security teams know whether their autonomous response is trustworthy?
A: They should test whether the response layer can identify, classify, and explain the impact of actions before it executes them.
Q: What fails when AI agents act on incomplete asset context?
A: The failure is not just bad prioritisation.
Practitioner guidance
- Establish a complete asset inventory across all connected domains Map IT, OT, IoT, and IoMT assets into a single governed inventory so agentic workflows operate from the same source of truth as human operators.
- Classify assets before allowing autonomous containment Require the response layer to distinguish device type, operational role, and dependency before an AI agent can isolate, segment, or quarantine anything.
- Use network segmentation to bound autonomous mistakes Place reversible containment policies at the network layer so an incorrect agent action cannot propagate beyond a tightly defined blast radius.
Bottom line: AI agents intensify a longstanding governance problem: incomplete visibility makes automated security decisions less trustworthy, not more.
Glossary terms: Agentic security, Universal Zero Trust Network Access, Blast Radius
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This topic was modified 6 days ago by NHI Mgmt Group
23/10/2026 9:49 am
Universal visibility, not more automation, is the real control problem in agentic security. AI agents can accelerate decisions, but they cannot improve decisions when the asset picture is incomplete. In hybrid environments that include IT, OT, IoT, and IoMT, the governance failure is blind action, not slow action. Practitioners should treat universal discovery and classification as the foundation of trustworthy autonomous response.
A question worth separating out:
Q: How should organizations approach the governance of AI agents?
A: Organizations should adopt a governance framework that incorporates continuous visibility, adaptive IAM practices, and stringent policy-based controls. This ensures that all agent actions are tracked, authorized appropriately, and assessed for compliance.
👉 Read our full editorial: Universal visibility is the control layer for agentic security
This post was modified 6 days ago by NHI Mgmt Group