Digital twins in cybersecurity promise to free up teams to focus on what matters

COMMENTARY: A "digital twin" – a construct borrowed from engineering and manufacturing – has had an extended moment across cybersecurity and beyond, with Gartner scoping the market for simulated digital twins to reach $374 billion by 2034.

But in cybersecurity and most of IT, every buzzword promises transformation. Are digital twins just another vector for tech complexity, or can digital twins offer a smarter, more strategic way to stay ahead of evolving threats?

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Security teams today are drowning in data. Alerts, vulnerability reports, endpoint logs, threat feeds: there's no shortage of information. Turning that flood into coherent, timely, and actionable intelligence remains a massive challenge.

Despite years of investment in tooling, most enterprises still rely on siloed systems to understand their attack surface. Vulnerability scanners operate separately from identity systems. Cloud configurations live in dashboards siloed from endpoint telemetry. This results in an incomplete picture—a disjointed view that leaves operational blind spots and encourages reactive, rather than strategic, security.

Every enterprise security professional recognizes this frustration. An admin chases down one alert, only to find it's a dead end. A critical vulnerability gets patched, only for the team to learn it was never exploitable by an attacker. Meanwhile, many security pros burn precious hours stitching together partial insights, trying to create a unified threat picture that never fully materializes. That's where a digital twin approach offers promise—not as a magic bullet, but as a model or way to visualize data in a way that breaks this cycle.

In the cybersecurity context, digital twins are continuously updated models of an environment that integrates infrastructure data, configurations, user behavior, and known exposures. It doesn’t just aggregate data — it contextualizes it. The difference between aggregation and contextualization is fundamental to understanding why digital twins represent a paradigm shift. Traditional security tools excel at data collection, but massive datasets don’t necessarily ensure actionable insight.

Data aggregation simply pulls information from multiple sources into a centralized location. Think of it as dumping puzzle pieces from different boxes onto the same table. There are more pieces in one place, but teams still don't know how they fit together or which ones actually matter for completing the picture.

Contextualization is where digital twins shine. They don't just collect that vulnerability scan showing a critical Apache server flaw—they map how that server connects to the company’s domain controller, which users have access, what data flows through it, and crucially, whether an attacker could actually reach it from an entry point they control. The digital twin models the relationships, dependencies, and realistic attack paths that transform isolated data points into strategic intelligence.

This contextual modeling happens continuously, updating as an environment changes—new cloud instances spin up, users change roles, patches get applied. The digital twin doesn't just reflect what the infrastructure looked like yesterday; it models how it behaves today and predicts how attacks might unfold tomorrow,  allowing for smarter remediation. Even better, this modeling happens safely outside of production. No active scans. No potential for system slowdowns. No unintended compliance alarms. Think of it as a dress rehearsal for a breach—minus the breach.

However, implementation isn't trivial. Building an accurate digital twin takes a complex build that requires buy-in from IT, cloud teams, development, and SecOps, not to mention high-quality data across environments. Done right, the approach lets security teams spend less time on data wrangling and more time asking strategic questions:

Organizations will need this proactive mindset as IT complexity increases. Hybrid environments, third-party integrations, and constantly shifting assets mean that static inventories and linear risk assessments no longer cut it. Modeling risk dynamically — based on how an environment would actually behave under attack in the moment, helps to level the playing field in favor of defenders.

The question isn't whether digital twins will transform cybersecurity, it's whether organizations will implement them thoughtfully enough to realize their potential. In a threat landscape that evolves faster than traditional defenses can adapt, the ability to model, simulate, and act promises to become the difference between staying ahead of attackers and perpetually playing catch-up.

When the rubber hits the road, it’s not the buzzword that matters: it’s the execution. Done right, digital twins won’t just help teams visualize risk—they’ll help organizations reduce it.

So take the time to build them correctly.

Jason Fruge, CISO-in-Residence, XM Cyber

SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Each contribution has a goal of bringing a unique voice to important cybersecurity topics. Content strives to be of the highest quality, objective and non-commercial.