IP Fabric has released version 8.1 of its network visibility platform, adding application-aware infrastructure mapping and a redesigned model for cloud environments. The update is designed to show how business applications depend on network devices, cloud resources, traffic paths, and security policies.networkworld
From network maps to application dependencies
IP Fabric creates a read-only digital twin of enterprise infrastructure by discovering devices, configurations, routes, and traffic paths. Until now, that view focused primarily on the network itself, while application information was usually stored in separate APM systems, CMDBs, and security platforms.
Version 8.1 adds Application Infrastructure Mapping, or AIM. The feature correlates application inventories with IP Fabric’s network model, allowing teams to see which infrastructure carries an application’s traffic.docs.ipfabric
Applications, workloads, interfaces, and flows are treated as platform objects. For each application, IP Fabric can calculate the end-to-end paths used by its workloads and assemble the results into a dependency map.
The mapping can be queried in both directions:
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Which network and cloud resources support a particular application?
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Which applications depend on a specific device or link?
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What could be affected before a device is upgraded or taken offline?
The platform can also produce recurring dependency reports and compare infrastructure before and after a change.
How the application data is added
IP Fabric does not discover application information directly from the network. Instead, AIM imports data from external sources.
The initial integration includes Illumio, while additional integrations are planned. Organizations can also provide application data through CSV files, APIs, or manually maintained records.docs.ipfabric
This distinction is important: IP Fabric supplies the network and path analysis, while application inventories and dependency information come from connected systems.
A cloud-native infrastructure model
Version 8.1 also changes how IP Fabric represents cloud environments.
Earlier versions normalized cloud infrastructure into concepts modeled largely on traditional enterprise networks. The new approach treats cloud-specific objects as first-class entities, including:
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VPCs and VNets.
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Subnets.
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Virtual network interfaces.
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Route tables.
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Network peerings.
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Security policies.
The platform still provides a translation layer for components such as transit gateways and load balancers, but these objects are now represented within a cloud-native model rather than forced into an on-premises structure.natlawreview
This enables path analysis within an individual cloud, between peered networks, and across cloud and on-premises environments.
IP Fabric can also evaluate AWS Security Groups, network ACLs, and Azure Network Security Groups to determine whether traffic is allowed or blocked—and identify the policy responsible for the result.
Implications for automation and AI
IP Fabric already offers a hosted MCP server that lets AI agents query its infrastructure model. With version 8.1, those queries can include application dependencies and cloud-native path information.
That additional context could make automated troubleshooting and change validation more reliable. For example, an operations team could determine which applications would be affected by a routing change, or compare the current path with a previous snapshot after a network update.
The capability is especially relevant as AI agents gain the ability to interact directly with network devices. Before and after comparisons can help verify whether an automated change produced the expected result.
What comes next
IP Fabric says future development will focus on supporting larger and more complex environments, expanding cloud coverage, and adding integrations with more technology providers.
The broader direction is clear: network management platforms are moving beyond device inventories and topology diagrams. By linking applications to the infrastructure beneath them, IP Fabric 8.1 aims to give operations teams—and the AI systems assisting them—a more complete view of how enterprise services actually work.