By leveraging digital twin technology, network teams can model complex networks in software, avoiding the costly approach many enterprises currently take—either investing millions in shadow IT testing environments or skipping testing altogether.

Figure 1. Network Digital Twin Technology Faces Obstacles.

Although digital twin technology is widely adopted in manufacturing today, its potential for enterprise network environments is significant. However, several challenges must be addressed before it can achieve mainstream adoption. Figure 1 shows Network Digital Twin Technology Faces Obstacles.

The first step is creating a detailed mapping of the network. Much like Google Maps overlays driving directions, traffic updates, or nearby points of interest, digital twin technology allows network teams to overlay critical network information—such as software upgrades, firewall rule changes, updated network operating systems, vendor or tool consolidation, or network adjustments resulting from mergers and acquisitions.

Once the model is in place, teams can simulate different scenarios, test various approaches, make adjustments, and validate changes to ensure any rollout meets its objectives without causing disruptions, explains Maccioni, senior director of product marketing at digital twin provider Forward Networks.

However, digital twins are not real-time systems. “We don’t make changes. We’re read-only. We don’t alter the configuration of network devices,” Maccioni notes. Forward Networks does offer integrations with workflow automation tools like ServiceNow and the open-source automation engine Ansible.

Gartner analyst Tim Zimmerman adds, “These tools generally operate on near-real-time or snapshot-based data, which is useful for validation and documentation but limits their effectiveness for live troubleshooting or active incident response. This distinction matters. While digital twins can enhance planning and reduce the costs of change, they are not designed as operational tools for managing live networks.”

“As a result, adoption has mostly been confined to large, complex environments that can justify the investment in additional management software,” Zimmerman says.

Gartner estimates that organizations using network digital twins to model configurations and software or firmware updates can reduce unplanned outages by up to 70%. Zimmerman notes that 15% of security breaches are linked to cloud misconfigurations or reconfigurations, such as migrating on-premises applications to the cloud. Digital twin tools help ensure that network policies don’t interfere with data flows during such migrations. Other use cases Zimmerman highlights include capacity planning to anticipate future traffic and infrastructure needs, incident replay to analyze past outages or breaches, security posture validation through simulated attacks and firewall or segmentation testing, and simulating boundary conditions that may yield unexpected results.

Risk mitigation remains the primary driver for enterprise adoption, says Scott Wheeler, cloud practice lead at Asperitas Consulting, which offers network digital twins as a service. “It’s a way to test changes to ensure a project doesn’t disrupt operations.” For example, one global enterprise successfully consolidated four routing protocols into one using digital twin technology without any issues.

Failover scenario testing is another important application, Wheeler adds. While network engineers can design alternative traffic paths in case of component failures, real-world stress testing is often impossible. In one engagement, digital twins revealed previously unknown failure scenarios.

Maccioni observes that enterprise interest spans multiple use cases. Many start with firewall rule management—a task that can cost millions annually—before expanding into outage prevention, troubleshooting, and compliance. “Security is also becoming a key driver,” he notes, as digital twins provide a single source of truth that reduces friction between network and security operations teams during troubleshooting.

However, adoption faces several barriers. Major infrastructure and network management vendors do not currently include digital twins as part of their core offerings, requiring enterprises to work with third-party providers. Wheeler explains, “This is an entirely new project and environment, requiring significant effort.” There is also no standardized approach: Forward Networks, for instance, uses a proprietary Header Space Analysis method developed at Stanford to create virtual network replicas, while Asperitas relies on the open-source EVE-NG framework, which requires manual coding and constant updates.

Deployment is complex and costly, often limited to single-vendor components or specific segments like a campus network. Zimmerman identifies three levels within a campus: configuration and policy validation, single-vendor equipment upgrades, and multi-vendor migration or replacement.

Looking ahead, Gartner notes that increasing network complexity, heightened cybersecurity risks, and a shortage of skilled personnel are driving the emergence of digital twins for network resilience and operations planning. Over the next 3–5 years, Gartner expects adoption to focus on parts of campus networks, expanding to full enterprise networks within a decade.

Maccioni says adoption has been slow because the concept is new to many network engineers, but awareness is growing, and budgets are being allocated. Wheeler adds that vendor support is limited, but removing barriers could accelerate adoption. Zimmerman emphasizes that broader use will require the ability to model composite networks spanning multiple vendors.

Unlike manufacturing environments, which are controlled and predictable, global enterprise networks are dynamic, with tens of thousands of endpoints and constantly changing data paths. The ultimate goal, Zimmerman says, is a digital twin that allows IT leaders to test daily operational workflows across their entire network, simulate any operating system or configuration changes in real time, and test boundary conditions that currently require manual configuration.

Source: NETWORK WORLD

Cite this article:

Priyadharshini S (2025), Digital Twin Technology for Networks Encounters Challenges, AnaTechMaz, pp.233