5 Ways AI in Network Operations Is Changing NetOps Right Now

A raw CVE list tells a team almost nothing useful on its own. It doesn't say which devices are running the affected software version, whether those devices are exposed in a way that matters given their role and placement in the network, or what to do next. Someone has to manually cross-reference the report against the network's current inventory, often across multiple vendors with different naming conventions, version numbers, and documentation formats. That process alone can take days or weeks before a single fix can be applied.

Knowing a vulnerability exists somewhere in the world isn't the same as knowing it exists on your network and is exploitable in production. Kilter AI maps published vulnerabilities against a network's actual device details, correlating threat intelligence from sources like CISA, NIST, NVD, and vendor feeds with what's really running, and prioritizes remediation based on genuine network risk rather than a generic severity score. From there, it recommends the best upgrade path, whether a patch or a workaround, and converts that into a set of logical, executable steps. What used to take a team days or weeks of manual cross-referencing now takes hours.

Every network vendor has its own command syntax, its own configuration structure, and its own nuances between versions. There's no universal factory default across vendors, which means an engineer troubleshooting an unfamiliar device type is often starting from scratch: hunting through vendor documentation, searching forums, or asking a colleague who happens to have touched that device before.

AI in network operations changes that by making configuration knowledge searchable across the entire fleet at once, regardless of vendor. Rather than an engineer needing deep familiarity with every device type in the environment, AI models can locate specific settings and commands across the full inventory and highlight the appropriate upgrade or configuration path for that exact device and release combination. Engineers stop re-learning vendor-specific nuances every time they touch an unfamiliar device, and the knowledge gap between the network's most experienced engineer and other team members starts to close.

Finding a single misconfigured or non-compliant device is only half the problem. The harder question is whether the same issue exists somewhere else in the environment, on a different device from a different manufacturer, running a different software build. Manually checking that across a multi-vendor fleet means consulting separate documentation for every vendor involved, and it's exactly the kind of task that gets skipped when a team is already stretched thin.

AI in network operations flips that process from reactive to proactive. Rather than waiting for the same misconfiguration to surface again on a different device, AI models can check the entire fleet against critical configuration parameters and vendor-specific data, flag whether a fix identified on one device should be extended to similar devices elsewhere in the environment, and allow that fix to be applied and automated consistently across every affected device at once, rather than one at a time as each instance is discovered.

Historically, building a reliable automation chain, one that logs in, runs a backup in case an issue requires the need to restore, executes a command, validates the result, runs another backup, and logs out safely, required real scripting expertise. Teams without a dedicated automation engineer either didn't build these workflows at all and continued to rely on manual methods or built brittle automations that broke the first time a vendor updated its command structure or changed how a device stored its settings.

AI in network operations serves as an informed assistant here, helping build, translate, and create multi-step chained automations built on best practices. Rather than starting from a blank script every time, AI draws on a library of previously used automations, identifying the discrete tasks that, when combined, closely match the steps required for a broader automation process. Network engineers can create production-ready workflows without needing deep automation skills, which shifts automation-building from a specialized capability held by one or two people on the team to something a much broader group of engineers can use safely.

The fastest way for AI in network operations to become a liability rather than an asset is for it to make changes nobody asked for, based on data sources and reasoning nobody can see or verify. That's the fear driving a lot of legitimate skepticism about AI in this space, and it's a reasonable one. A network is not a low-stakes place to let a system act autonomously without oversight.

Responsible AI in networks is built around trust as a design principle, not an afterthought: trustworthy by design, consistently accurate, human-centered, and transparent about how a given recommendation was generated. That means AI is positioned to deliver insights, recommendations, and automation assistance, not to make unilateral changes to production infrastructure without a human confirming the next step. NetOps teams stay in control of what happens, validating insights and confirming next steps or asking for alternatives.

In a recent Packet Pushers podcast, Akamai Field CTO Irfahn Khimji explained, “Without AI-powered automation, networking teams are struggling to keep up with the pace of change. A prospect recently said they submitted a business case for 23 FTEs across their operations in Canada, the U.S., and Mexico to keep up. Even if the additional headcount had been approved, finding that many qualified engineers is a huge challenge. On a smaller scale, another organization with 2,000 network devices is required to make two changes per day on every single switch. They determined their manual approach was costing them about $8 million annually.”

Whether teams are trying to get their arms around increasing vulnerabilities, configuration drift, multi-vendor complexity, automation demands, or AI reliability, none of these five shifts in NetOps teams' duties are hypothetical or years away. AI is already helping teams change how they spend their time, moving away from hours of manual research, cross-referencing, documentation hunting, and one-off script writing, and spending more of their time on higher value work that requires human judgment. As CVE volume keeps climbing year over year and networks keep adding vendors, device types, and environments, the gap between what manual review can realistically handle and what the job actually requires keeps widening, not narrowing.

AI in network operations doesn't close that gap by working faster than a person at the exact same task. It closes it by handling the parts of the job that don't need a person in the first place, freeing up the team to focus on the parts that genuinely do.

Kilter AI is the intelligence layer of the Kilter Platform, built by BackBox to bring these five capabilities into a single, unified system rather than a patchwork of point tools. It uses validated data, context, and best practices to deliver trustworthy AI-powered insights, recommendations, and automation creation that helps NetOps teams strengthen vulnerability and configuration management, enforce compliance, streamline operations, and reduce manual effort. Built on BackBox Principles of Responsible AI, Kilter AI is trustworthy by design, consistently accurate, human-centered, and transparent.

Teams running Kilter typically reduce the cost of network operations by 76% and compress jobs that used to take hours into minutes.

Schedule a 30-minute demo to see Kilter in action.