Managing multi-vendor networks is challenging. This difficulty arises not only from the number of vendors involved but also from the wide range of device types, models, and software versions layered on top of one another. Even highly capable NetOps teams typically manage only three to five vendors effectively. Additionally, many organizations keep older hardware in secondary roles rather than retiring it, resulting in tens of thousands of devices and version combinations in larger networks.
That complexity used to be the main concern for network leaders. However, the real threat now isn't complexity — it's speed. In a recent Network World article published on September 11, BackBox VP of Product and Engineering Richard Phillips discusses how new CVEs are appearing faster than ever, and AI-driven vulnerability discovery tools are shrinking the time between discovering a flaw and creating an exploit. A manual, tool-by-tool review simply can't keep up.
Where AI Actually Helps
AI isn't about giving up control to self-healing, autonomous systems. That trade-off (speed for control) understandably makes most NetOps teams nervous. The true value appears in the unglamorous but essential work behind every decision.
- Streamlining vulnerability analysis. Instead of manually checking CVE feeds against your device inventory, AI can gather and standardize that data, add environmental context, and highlight what truly matters for your network, in minutes rather than days.
- Extending checks across vendors. Once you identify a single device or vendor affected, AI can apply the same check across your entire fleet, flagging other devices that share the same exposure or have drifted out of compliance.
- Recommending a path forward. AI can assemble available remediations like patches, workarounds, upgrades, or config changes, and explain its reasoning so an engineer can verify or push back before taking action.
- Linking actions to a process. With human validation at each step, AI can connect backup, change, compliance check, and verification tasks, transforming a single fix into a repeatable, auditable process that can scale across a diverse vendor fleet.
The pattern across all of this: AI handles the repetitive, structured tasks with consistency, scale, and speed, while engineers keep the judgment calls and tribal knowledge where they matter most.
How BackBox Helps
BackBox's Kilter AI operates on the same principle: AI as a reliable advisor rather than an autonomous decision-maker. For Kilter's main use cases, you get:
- AI Insights that verify which devices a vulnerability actually affects, gather device-specific details (make, model, firmware, configuration), and flag available workarounds, along with compliance status across vendors.
- Recommendations that identify upgrade paths, configuration alignment, and other devices likely impacted by the same issue, so teams aren't chasing risk one ticket at a time.
- Automation creation assistance that transforms validated remediation steps into production-ready, human-in-the-loop chains without requiring deep scripting expertise from every engineer on the team.
The goal is to reduce a process that used to take weeks or months — such as reviewing vulnerability reports, assessing their relevance, and developing fixes — to just hours, all while keeping a human in control of every critical step. In a multi-vendor environment where speed is now the top risk, that combination of reliable AI and human oversight enables NetOps teams to build and maintain a resilient network.
Read the full article or discover how Kilter leverages AI to enable NetOps teams to automate network device lifecycle management and security operations; visit our platform page. Ready to get started? Request a demo to see Kilter in action.
Written by
Stephanie Stouck