At the 2025 ITWeb Security Summit, Snode Technologies unpacked an innovative approach to cyber defence: leveraging digital twin technology to help organisations proactively detect, simulate and neutralise threats in real-time. This challenges the traditional frameworks that favour increasingly inefficient reactive security measures.
Threats are becoming more complex, and emerging technologies (like quantum computing) change attack surfaces in ways we don't yet understand. That's why Snode Technologies, an award-winning cyber defence firm, is working with digital twin technology to help secure its customers' environments with greater impact. Digital twins, a virtual clone of an organisation's digital infrastructure, enables simulated attacks and proactive defence strategies without compromising or disrupting tangible assets.
According to Nithen Naidoo, founder and CEO of Snode, the company has been exploring and developing digital twin approaches to cyber security since 2022. “We’re no longer waiting for cyber breaches to happen,” said Naidoo. “Digital twins allow us to simulate breaches, understand threat behaviour and test countermeasures without risking the live infrastructure.”
Digital twin technology, initially used in industrial and physical asset modelling, has been reimagined by Snode to serve cyber environments. The model replicates network architecture, assets, vulnerabilities and threat vectors. Unlike traditional honeypots, which are typically generic traps, Snode’s digital twin capability mirrors the actual configurations and vulnerabilities of the live system, offering much higher fidelity and threat intelligence value.
CTO Stephan Krynauw added: “Attack path simulation and digital twin deception networks can be applied across on-premises, cloud or SaaS infrastructure. However, the effort and payoff vary depending on the set-up."
More broadly, standard-setting bodies and industrial sectors are recognising digital twins as a strategic asset in the cyber arms race. According to researchers at NIST and the University of Michigan, integrating digital twins with AI and human expertise allowed them to successfully detect cyber attacks on 3D printer systems, demonstrating that digital twins can accurately flag malicious anomalies that network-based monitoring might miss.
What makes Snode’s implementation unique is its ability to tailor digital twins to diverse client ecosystems, integrating threat modelling, asset discovery and risk prioritisation into a single continuous simulation loop. This is particularly useful in the new evolution of cyber security, where the battleground may be virtual, but the consequences are not.
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