They struggle because mixed estates combine managed endpoints, unmanaged appliances, and fragile operational devices under one policy model. That creates visibility gaps, inconsistent enforcement, and competing team priorities. When the asset base is heterogeneous, policy design has to start with what can actually be observed and controlled, not with an idealised architecture diagram.
Why This Matters for Security Teams
Segmentation fails fastest when teams assume one control model can fit every device class. Managed laptops, servers, OT controllers, printers, medical devices, and embedded systems often differ in patch cadence, protocol support, authentication options, and logging depth. That means the same segmentation rule can be too permissive for one asset type and too disruptive for another. NIST’s NIST Cybersecurity Framework 2.0 is useful here because it pushes teams to understand assets, dependencies, and risk before enforcing boundaries.
This matters even more when mixed environments include identity-bearing services and automation. NHIMG’s Ultimate Guide to NHIs notes that NHIs outnumber human identities by 25x to 50x in modern enterprises, which is a reminder that device segmentation is often also identity segmentation. If service accounts, API keys, and device credentials are not visible, network zoning becomes a partial control rather than a reliable one. In practice, many segmentation programmes fail not because the architecture is wrong, but because the estate is messier than the policy model assumed.
How It Works in Practice
Effective segmentation in mixed-device environments starts with asset classification and communication mapping, not subnet carving. Security teams need to separate what can authenticate, what can be monitored, and what can only be constrained at the network layer. Managed endpoints may support certificate-based access, EDR-backed posture checks, and dynamic policy. Unmanaged or legacy devices often cannot. That is why current guidance suggests building policy tiers around device capability, criticality, and traffic patterns rather than applying a single flat rule set.
The practical sequence usually looks like this:
- Identify device classes and the services they must reach, including identity and secrets dependencies.
- Use NIST Cybersecurity Framework 2.0 to anchor inventory, protective control design, and monitoring outcomes.
- Apply stronger controls to managed assets first, where authentication, telemetry, and policy enforcement are measurable.
- For fragile or unmanaged devices, constrain east-west traffic tightly and allow only explicit protocols and destinations.
- Validate that non-human credentials, service accounts, and tokens used by devices are rotated and scoped appropriately, as described in Ultimate Guide to NHIs.
Where teams often go wrong is treating segmentation as a one-time network project instead of an operating model. Mixed estates need change control, exception handling, and telemetry feedback loops so that policy can evolve as devices are replaced or repurposed. That also means accepting that some segments will remain coarse because the devices inside them cannot support richer controls. These controls tend to break down when legacy OT, flat virtual networks, and shadow IT devices share the same broadcast or trust domain because the minimum-common-denominator design invites exception sprawl.
Common Variations and Edge Cases
Tighter segmentation often increases operational overhead, requiring organisations to balance reduced blast radius against supportability and uptime. That tradeoff is most visible in environments where vendors require broad reachability, scanners need passive-only access, or devices cannot survive frequent policy changes. There is no universal standard for this yet, so best practice is evolving around risk-based zoning rather than absolute isolation.
Edge cases include safety systems, medical equipment, industrial controllers, and building management platforms. In those settings, the goal is usually containment, not perfect per-device identity enforcement. Teams may need compensating controls such as protocol allowlisting, jump hosts, brokered access, or one-way monitoring. For identity-heavy environments, especially where NHIs drive device communications, the segmentation design should explicitly document which credentials map to which traffic flows and how those secrets are governed. NHIMG data shows 96% of organisations store secrets outside of secrets managers in vulnerable locations, which makes hidden device credentials a segmentation risk as much as a secrets risk.
For practitioners, the key question is not whether segmentation is possible, but which parts of the environment can support fine-grained controls today. Where devices are unmanaged or brittle, keep the boundary simple, measure it continuously, and plan migrations that reduce exception debt over time.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and CIS Controls set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM-1 | Mixed-device segmentation depends on knowing what assets exist and how they behave. |
| MITRE ATT&CK | T1021 | Lateral movement is the core threat segmentation is meant to constrain. |
| CIS Controls | Control 4 | Asset inventory is essential when device types and capabilities are mixed. |
| OWASP Non-Human Identity Top 10 | Device segmentation often fails when service accounts and API keys are not governed. |
Inventory device classes, map dependencies, and base zones on observed risk rather than assumptions.
Related resources from NHI Mgmt Group
- Why do mixed device environments weaken Zero Trust models?
- How should security teams govern smart device identities in mixed-vendor environments?
- Why do mixed IT and OT environments break traditional segmentation assumptions?
- Why do network-based segmentation models struggle in modern healthcare environments?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on July 10, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
