Enterprise IT doesn’t look the way it did a decade ago, back when Gartner first coined the term “AIOps.” Today, with services spread across public clouds, private clouds, virtual machines (VMs), containers, and edge environments, it’s no surprise that IT leaders are turning to automation to gain more confident and cost-effective control over their network.

In our recent webinar, the team at Red Hat pulled back the curtain on their AIOps journey. They shared what drove them to pursue it, where the biggest roadblocks emerged, and which tools helped them move forward with confidence. Read on to explore the challenges they tackled—and how IP Fabric became the “glue” that made safe, reliable AIOps a reality. 

Overcoming the AIOps Paradox

It’s a truth universally acknowledged that the more distributed an network becomes, the more difficult it is to predict how it’ll behave. And without a solid understanding of how devices, services, and applications are connected, any automated action becomes a high-stakes roll of the dice, introducing the risk for errors that can cascade into costly outages, security gaps, or compliance violations.

This is precisely what Red Hat aimed to avoid as they methodically charted their path towards AIOps. Though they had a vast quantity of network data from ServiceNow, NetBox, OpenShift, Kubernetes, VMs, and more, it was fragmented across teams and tools. Martin Moucka, IT Manager at Red Hat, knew that context was a crucial ingredient to successful AIOps, and aimed to create a more complete picture of network behavior by connecting their raw data to the network layer.  

Enter: dependency mapping.  

Using IP Fabric’s digital twin capabilities, Red Hat was able to map, standardize, and validate their entire network. This process not only transformed their data into actionable, automation-ready knowledge, but also unlocked a new level of control by understanding the implications of any change to network behavior.  

Building a Baseline for Network Automation

Before Red Hat could scale AIOps across their environment, they needed a dependable foundation: continuously updated network intelligence that every team and system could rely on. IP Fabric’s digital twin capabilities made that possible by empowering Red Hat to proactively assess the ripple effects of any automated change, identify upstream and downstream dependencies, and give all stakeholders the information they needed in a language they could understand. 

Visualize Your Network From End to End

As Moucka puts it, “You cannot automate what you don’t understand.” IP Fabric helped with the latter by automatically discovering and documenting a complete inventory of network devices, and mapping their connections and configurations from end to end. 

IP Fabric conducts this automated discovery by: 

• Leveraging read-only credentials to connect to network devices via CLI commands and APIs. 
• Gathering operational data like interface states, ACLs, and configuration and inspection rules. 
• Identifying and connecting to “neighbors” using protocols like LLDP, CDP, ARP, routing tables, and traceroute. 
• Building Layer 2 and Layer 3 topologies by analyzing traffic flows, MAC tables, and spanning-tree and routing protocols. 
• Collecting timestamped data to track network changes and compute cross-technology dependencies. 
• Inventory all discovered devices within the administrative domain. 

This complete visibility gave Red Hat a strong foundation for the next phase of automation. 

Standardize Your Data for Automation Readiness

Raw network data rarely fits neatly into automation or analytics workflows. IP Fabric addressed this by standardizing all inputs from inventories as well as from MAC and ARP tables, and automatically feeding that standardized data to other tools that Red Hat’s team was already using, like ServiceNow, NetBox,  OpenShift, and Ansible.  

Ensure Your Data is Always Up to Date  

Red Hat regularly validates their network map and tables in each network snapshot, which they run approximately every four hours. This living timeline of the network helps Red Hat to ensure that their automated decisions are based on the most up-to-date information. It also empowers Red Hat to analyze their network behavior over time. For instance, when challenges to security or compliance arise, these snapshots also act as detailed records to:  

• Pinpoint when and where network behavior changed, whether it’s a device that’s reaching End-of-Life (EoL) or a firewall that’s been bypassed.  
• Provide timestamped proof of compliance with security frameworks and regulatory standards.  

With each end-to-end snapshot, IP Fabric shares highly contextualized data with any tool or person that needs it. This enables Red Hat to launch reliable AI workflows where every change is informed, auditable, and ready for the next wave of innovation. 

TL;DR: 3 Steps to Start Your AIOps Journey 

Whether you’re just beginning to explore AIOps or looking to refine your approach, these are steps that Red Hat followed to success: 

1. Start with a solid foundation: Build a complete map of your network’s devices, connections, and configurations, and use that as your starting point. 
2. Find your “glue”: Identify tools like IP Fabric that standardize and unify your dataset.  
3. Automate with context: Leverage a deep, contextualized understanding of network behavior to build towards more proactive, automated workflows.  

Get the full story from Red Hat and IP Fabric by watching our full 40-minute webinar on demand.