Network complexity is outpacing NetOps teams: How AI can help

Network infrastructure owners often tell me that the biggest obstacle to network cyber resilience is the complexity of multi-vendor network environments. They aren’t just referring to the number of vendors in their environment, but also to the variety of device types (firewalls, routers, switches and access points), models and software versions. Well-resourced organizations with vendor-specific expertise on their NetOps team can usually manage three to five vendors at most. Beyond that, the talent, skill and training required to handle greater complexity are often beyond their reach.

While it’s difficult to assemble a team with depth and breadth of expertise, it’s even more challenging for that team to have the capacity to proactively manage risks across complex networks. Do the math and it becomes obvious that the numbers work against us. For a large healthcare provider with over 20,000 devices from five main vendors, comprised of approximately 200 different device types, models and versions, there are tens of thousands of permutations — conservatively.

Adding another point to the discussion, large organizations that have been around for years often have multiple generations of some products. Rather than retire a three-year-old firewall, they move it internally where it can still add value, and NetOps teams keep it operational and secure for as long as possible. The complexity of managing these environments is growing exponentially and overwhelming teams.

The need for speed is real, and AI can be that accelerator

AI adoption for network management has been slowed by interoperability gaps across vendors and a heavy reliance on tribal knowledge held by senior engineers. Understandably, teams hesitate to trust automated or AI‑driven decisions that can impact networking infrastructure, but that’s not where AI shines. 

Instead, teams should focus on the data that underlies the decisions and standard operating procedures (SOPs) that strengthen network cyber resilience. Using AI for knowledge gathering and repetitive tasks gives humans more time for productive work, where detailed operation and organizational knowledge is crucial. AI provides consistency, accuracy, scalability, attention and cost-efficiency for structured tasks, while humans apply their expertise for oversight and decision-making.  

Gartner predicts AI agents will be implemented in 60% of all IT operations tools by 2028. Let’s explore the core value AI offers NetOps teams responsible for managing multi-vendor networks.

Focus on what matters most to your organization

A few common examples include vulnerability prioritization, compliance monitoring and live device checks.

• Scouring multiple trusted sources for news on the latest common vulnerabilities and exposures (CVEs) for different devices takes hours, if not days, of manual data gathering and analysis. The emergence of AI-driven vulnerability discovery capabilities such as Mythos underscores the need to compress the timeline from data aggregation to remediation. AI can aggregate and normalize data, add context to prioritize in your environment and deliver actionable insights in minutes, so you can focus on the vulnerabilities that matter.
• If you prioritize vulnerabilities, compliance becomes your first line of defense. It protects you against CVEs by strengthening your security posture. In fact, 87% of CEOs say that cybersecurity and privacy regulations effectively reduce organizational risk. AI can monitor configurations for alignment with your compliance regime (including regulatory requirements, industry standards like CIS benchmarks or your organization’s best practices) and immediately alert on exceptions, suggest actions and send detailed reports.
• Increasingly, NetOps teams need the ability to perform live checks to verify device status or to rapidly respond to a list of security team requests triggered by an audit. AI can check all your devices for the latest backups, current configurations or alignment with compliance policies, and notify you of the status. It can even send real-time notifications whenever a device changes.

Apply that logic across other vendor devices to proactively check status

Now that you have the status for one group of devices, the questions that typically follow include:

• Are my other vendors and products impacted by the same CVE?
• Do my other devices align with my compliance checks, and do they match the golden configuration I have set for this vendor and product?
• Can you tell me if any of my devices have drifted from their expected configuration?

AI can collect relevant data from other network devices you’re interested in and display the status. You quickly find out whether devices from other vendors are affected by the same CVE, have passed or failed a compliance check, and whether a configuration needs updating.

Get recommendations for remediation

The next step is to use the data to determine how to remediate. AI can compile information on available remediations from trusted sources and recommend actions. Often there are several options: a patch, a workaround, an upgrade or a configuration change. As an expert, you should ask AI to explain the reasoning behind the recommended device change, so you can verify the remediation or request an alternative. AI can also compile a list of commands from device manufacturers’ web pages and technical documentation, providing the steps to remediate a vulnerability or bring devices back into compliance.

Chain automated tasks to reinforce SOPs and accelerate action

Regardless of the type of change required, AI can help accelerate the process with a human in the loop to validate or adjust each step as needed before deciding to automate. You can chain actions together using AI to support automated tasks aligned with SOPs and ensure auditability. Actions typically include: completing a backup in case recovery is needed, performing the task to make the change, running a compliance check and testing to confirm the change took effect. A multi-step, trust-but-verify approach gives teams a head start and the confidence they need to apply a change across a wider fleet of network devices.

Network complexity will continue to grow, but it doesn’t have to hinder network cyber resilience. Using AI to bridge vendor fragmentation and reinforce standard operating procedures gives NetOps teams the capacity and confidence to proactively mitigate risk across multi-vendor networks — at scale.