What’s a Digital Twin?

What is a Digital Twin?

A digital twin is a live model of an organization’s environment. It shows how systems, users, and assets connect across the network. The model updates continuously using real infrastructure data. Security teams use digital twins to map exposures, trace likely attack paths, and focus on areas with the greatest risk.

A digital twin model offers a clear and safe way to explore how an environment behaves. Teams can walk through simulated incidents, test response options, and study outcomes without impacting production systems. The digital twin presents everything in context so that decisions reflect how the environment actually works.

A digital twin operates as a full and ongoing snapshot of an environment, as opposed to a static report. Teams can see how vulnerabilities connect, where attackers might move, and how risk builds across systems. Live data keeps the model current – enabling teams to highlight patterns, detect early signs of trouble, and reveal weak points. 

Teams use digital twin models to simulate attacks, validate responses, and improve planning. It brings together everything that matters into one continuous view. This gives security teams the confidence to act, the insight to prioritize, and the space to plan ahead.

How Do Digital Twins Work?

A digital twin builds a live view of your cybersecurity environment. It brings together data from networks, systems, identities, and configurations – and keeps everything up to date automatically. This removes the blind spots that often come with disconnected tools or stale snapshots.

A digital twin allows teams to track the live state of an environment. When configurations shift or new exposures appear, the twin reflects those changes immediately. Teams gain access to an always-current map of risk, not a frozen point in time. The World Economic Forum notes that this kind of continuous visibility supports faster and more confident decision-making across the business.

Some cybersecurity digital twins include attack path modeling. In these cases, the twin shows how an attacker could move laterally through the environment by linking exposures, misconfigurations, or excessive privileges into a viable path.

AI and machine learning add an additional layer of insight in digital twin environments. These tools scan for unusual behavior, recognize patterns, and detect emerging risk. This gives security teams more time to act and greater precision when deciding how.

What Are the Key Benefits of Digital Twins?

Digital twins help security teams see their environments clearly, act quickly, and plan with confidence. They bring structure and context to complex systems and provide a safe way to analyze, simulate, and improve defenses without touching production.

Key benefits include:

Safe and Continuous Assessment

Digital twins analyze vulnerabilities without scanning live systems. Security teams gain real-time visibility without introducing risk or disruption.

Faster Incident Response

The digital twin shows how an attack moves through the environment. Teams can test responses and act quickly with a clear view of what’s happening.

Stronger Security Drills

Simulated attacks let teams practice real scenarios. Exercises based on actual systems build confidence and improve team coordination.

Smarter Continuity Planning

The digital twin maps how disruptions affect operations. Teams can test recovery plans and make improvements before problems occur.

Accurate Exposure Detection

By running continuously, the digital twin surfaces exposures as they appear. This helps teams close gaps that routine scanning might miss.

Early Threat Detection

AI spots patterns and warning signs that point to potential attacks. Teams can focus on risks that match known tactics.

Response Testing Without Risk

Teams can try out response plans in a safe environment before making changes to live systems. This helps improve speed and accuracy.

Post-Incident Simulation and Learning

After an incident, digital twins recreate what happened. Teams can review decisions, measure outcomes, and strengthen protocols based on real conditions.

How Do Digital Twins Compare to Traditional Data Aggregation?

Traditional data aggregation approaches create several challenges that digital twins overcome:

Where Are Digital Twins Being Used Today?

Digital twins are no longer limited to engineering or research labs. They are now embedded in critical sectors where simulation and continuous modeling improve performance, safety, and resilience.

• Manufacturing. Plants use digital twins to test production changes, plan maintenance, and cut downtime. Deloitte’s 2025 Smart Manufacturing Survey lists simulations and digital twins among the advanced technologies shaping the future of manufacturing. Nearly eight in ten executives say they are directing a large share of their improvement budgets toward initiatives that rely on them.
• Energy and utilities. Grid operators and power companies use asset-level twins to model weather risk, schedule vegetation work, and harden networks. Southern California Edison, for example, lowered time invested in vegetation management by half across a large service area after adopting a 3D network twin.
• Healthcare. Providers and researchers build patient-specific twins to compare treatment paths and predict outcomes. Clinics use clinical twins that integrate data from medical devices, health records, and remote sensors to predict disease progression, tailor treatments, and improve surgical planning.
• Cities and infrastructure. Governments deploy city-scale twins to test zoning, mobility, and resilience policies before field changes. Virtual Singapore is a high-resolution urban model that supports scenario tests for infrastructure and emergency response. The UK National Digital Twin Programme sets a policy framework for an ecosystem of connected twins across sectors.
• Aviation. Airport authorities and regulators use digital twins to improve terminal flow, baggage systems, and safety analysis. 
• Financial services. Banks use financial and customer-journey twins for stress testing, risk modeling, and product design. For example, Atom Bank – a UK digital bank – developed a full-scale operational digital twin that supports financial planning, resource allocation, product pricing, and funding decisions.
• Cybersecurity operations. Security teams use environment-level twins to model threats and assess exposures across IT and industrial control systems. The World Economic Forum projects that the market for simulation-based digital twins will more than triple in size by 2032.

What Makes Digital Twins Strategic for Cybersecurity?

Digital twins shift cybersecurity from reactive defense to a more proactive strategy. They create space for real planning – by modeling real infrastructure, simulating live threats, and showing how risk flows across the environment in real time. 

This makes them more than a visibility tool. Digital twins help security teams make decisions that align with business risk. By highlighting choke points and showing how systems interact, some digital twins can reveal where a single control might block multiple attack paths. 

The twin also becomes a strategic planning tool. It supports continuous threat modeling without affecting operations. Teams can try out changes, review segmentation, and see the impact of updates before making anything official.

Finally, digital twins support post-incident simulation, which turns every incident into a learning opportunity. Teams can replay what happened, test alternate actions, and adjust their response playbooks with full context. An integrated digital twin solution transforms the cybersecurity landscape by automatically consolidating diverse data sources, visualizing attack paths, and enabling proactive, resource-efficient risk management – all without compromising your production environment.

All this makes digital twins strategic enablers – helping security leaders gain both a clearer picture of risk and the confidence to act on it.