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How Using a Digital Cyber Twin with Machine Reasoning Enables an Evolutionary Approach to Holistic Cyber Risk Management

In today's rapidly evolving digital landscape, cyber threats and attacks are becoming increasingly sophisticated and widespread, and organizations across all industries and sizes are at risk. To protect themselves, many organizations are turning to risk management approaches that enable a comprehensive view of their cyber risk, including TTP (tactics, techniques, and procedures) level Cyber Threat Susceptibility Assessments (CTSA). However, conducting these assessments can be challenging, especially in a dynamic and constantly changing threat environment. That's where using a Digital Cyber Twin with Machine Reasoning comes in.

A Digital Cyber Twin is a virtual replica of a physical asset or system, which can be used to simulate and analyze the performance of the real-world asset in a safe and controlled environment. Machine Reasoning involves using algorithms and models to make sense of complex data, and is used to enable a more dynamic and adaptive approach to cyber risk management.


Digital Cyber Twins use traditional Knowledge Representation and Reasoning (KRR) rather than virtualized IT because KRR is better suited for modeling and simulating complex systems and processes. KRR allows Digital Cyber Twins to create logical rules and knowledge bases that can be used to make inferences and draw conclusions about the performance and behavior of the system being modeled. By contrast, virtualized IT focuses on replicating the physical environment of an organization in a virtual environment. While virtualized IT is useful for testing and validating IT systems, it is less effective at modeling complex processes and interactions, such as those that are critical to cyber risk management. Digital Cyber Twins that use KRR are better able to simulate the behavior of the real-world system being modeled, which enables them to perform more accurate and detailed assessments of cyber risk.

By combining a Digital Cyber Twin with Machine Reasoning, organizations can continuously collect and analyze data from both the attack surface and the threat landscape, and update the TTP level Cyber Threat Susceptibility Assessment in real-time. This approach enables an evolutionary approach to holistic cyber risk management, by enabling organizations to continuously adapt their security controls in response to changing threat conditions and emerging threats.

One of the key benefits of using Machine Reasoning for TTP level Cyber Threat Susceptibility Assessment is that it enables a more dynamic and adaptive approach to cyber risk management. By continuously collecting and analyzing data, organizations can stay ahead of emerging threats and adjust their security controls in response to changing threat conditions. This approach can also help organizations to more accurately prioritize their efforts to mitigate risk, by focusing on the threats that are most likely to be successful based on the specific characteristics of their environment.

Moreover, using Machine Reasoning rather than machine learning for CTSA is a more effective approach because machine reasoning involves using logical rules and knowledge to make inferences and draw conclusions. This approach is more like human reasoning, which involves using knowledge and logic to make decisions. CTSA requires complex human knowledge that is filled with preconditions to determine if the TTP is even possible, which is difficult to capture and apply consistently. Machine reasoning allows for the creation of logical rules and knowledge bases that can be used to make inferences and draw conclusions about the likelihood and impact of different types of cyber threats. It enables Digital Cyber Twins to perform CTSA more effectively and accurately, by allowing them to incorporate a wide range of data sources and create complex rules that take into account preconditions and dependencies that are necessary for a TTP to be successful. This allows Digital Cyber Twins to more accurately assess the cyber risk of an organization and prioritize their efforts to mitigate that risk.

While machine learning is focused on identifying patterns in data, machine reasoning involves using logical rules and knowledge to make inferences and draw conclusions. This difference makes machine reasoning a more effective tool for CTSA because it enables the Digital Cyber Twin to create complex logical rules and knowledge bases, which can incorporate a range of factors and dependencies that are essential for accurately assessing the cyber risk of an organization. Machine reasoning can help standardize the CTSA process, reducing the risk of human error and ensuring that the results of the CTSA are reliable and repeatable.

In summary, using a Digital Cyber Twin with Machine Reasoning for TTP level Cyber Threat Susceptibility Assessment enables an evolutionary approach to holistic cyber risk management. By leveraging this technology, organizations can better protect themselves against a broad range of cyber threats, and respond more quickly and effectively to emerging threats as they arise. By using machine reasoning rather than machine learning for CTSA, organizations can more accurately and consistently assess the cyber risk of an organization, incorporating a range of factors and dependencies that are essential for accurately assessing the cyber risk of an organization. Digital Cyber Twins use traditional Knowledge Representation and Reasoning (KRR) rather than virtualized IT because KRR is better suited for modeling and simulating complex systems and processes. KRR allows Digital Cyber Twins to create logical rules and knowledge bases that can be used to make inferences and draw conclusions about the performance and behavior of the system being modeled. By leveraging a Digital Cyber Twin with Machine Reasoning, organizations can stay ahead of the curve when it comes to cybersecurity and ensure that they are well-equipped to defend against the latest threats and attacks.

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