Guide to using digital twins for cybersecurity testing

Before deploying a digital twin

Constructing accurate digital twin environments requires significant investment and planning. To be accurate, the virtual duplicates must rely on information drawn from multiple and diverse data sources. The data must then be normalized to make it useful to digital twin architectures.

Data sources include the following:

• Hardware and software asset inventories across on-premises, cloud and hybrid deployments.
• Security controls, including SIEM, endpoint detection and response, web application firewalls, security orchestration, automation, and response and next-generation firewalls. This defines the defensive capabilities of the production environment.
• Vulnerability and threat feeds to understand information sources for potential threats.
• User account and identity and access management data to establish user norms.
• Network traffic and device logs to understand network communications patterns.

Digital twin services

Digital twin services for testing, threat identification and predictive analytics are available from a number of providers, including Microsoft, Nvidia, AWS and IBM.

Digital twin services also exist for modeling and optimizing industrial and manufacturing environments. Others help organizations duplicate supply chain twins for additional visibility.

Cloud or on-premises deployments?

Most organizations opt for cloud-based digital twin deployments, although highly regulated industries might prefer on-premises alternatives.

In general, cloud deployments offer scalability and flexibility, cost-effective implementation and advanced cloud security tools, while on-premises deployments offer data sovereignty, reduced dependency on third-party providers, and more customized and stronger security controls.

The final decision on whether to deploy digital twins on-premises or in the cloud often depends on these, as well as cost and regulatory requirements.

Security benefits of digital twins

Digital twins are a proactive security approach that offer significant benefits, including vulnerability identification, predictive analytics and incident response testing.

Vulnerability identification

Security analysts use digital twins to identify exposures, including misconfigurations, missing patches and other potential issues. Running a simulated attack exposes these weaknesses, providing teams with the opportunity to mitigate them before malicious actors can exploit them.

This continuous assessment adapts to changes in both the production environment and potential risks, such as zero-day vulnerabilities.

Predictive analytics

AI-assisted digital twin deployments help identify potential attack vectors and prioritize risks to minimize exposure and structure response plans. They provide predictive analysis by combing through usage patterns and historical trends. This information could include mean time to failure, mean time between failures, mean time to detect and other similar metrics important to availability and incident response teams.

The real time nature of a digital twin helps facilitate quicker responses and more accurate assessments.

Incident response testing

Digital twins provide opportunities to test how security personnel, automated systems and AI respond to incidents.

• Validating manual testing. Rehearsing responses in realistic environments, improving coordination, communication and procedures.
• Validating automated testing. Testing automated security workflows for efficiency and effectiveness. Verifying these automated workflows helps avoid unintended consequences.
• Optimizing playbooks. Providing actionable feedback and results to cybersecurity responders, enabling the production of more effective playbooks. This post-incident review improves decision-making and root-cause analysis.

Organizations can use the feedback received from incident response testing scenarios to design more effective team training and post-incident analysis for specific types of attacks.

Digital twin best practices

Carefully plan and implement digital twin deployments to reap the benefits and minimize risks. The digital twin infrastructure itself cannot become a vulnerability, and it must also accurately reflect the production environment.

Use the following insights and best practices to manage your organization's digital twin deployments:

• Encrypt data transfers between production and physical twins.
• Consider phased deployments to demonstrate effectiveness and manage the project scope.
• Enforce strong access controls, including MFA and role-based access control.
• Implement threat modeling and mitigation strategies early.
• Place digital twins on isolated segments and eliminate network communications with production systems.
• Conduct continuous penetration testing to address current threats.
• Audit digital twin systems regularly.
• Ensure digital twins remain aligned with the production systems.
• Implement effective data governance and validation to ensure digital twin operations include trusted data.

Digital twins are beneficial beyond standard cybersecurity use cases. Not only do they simulate various attack scenarios, but they also provide planning and predictive maintenance information for manufacturing, supply chain and logistics planning, healthcare and training systems.

Expect digital twins to become increasingly crucial to cybersecurity practices, especially in high-security environments. It's time to evaluate how this technology can be applied in your organization.

Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget, The New Stack and CompTIA Blogs.