Advancing Cyber Risk Assessment with Explainable AI and Scientific Methodology

I. Introduction

Over the last decade, the cybersecurity industry has predominantly relied on quantitative analysis of historical data to measure and manage cyber risk. While this approach is useful in understanding past trends and patterns in cyber attacks, it may not provide an accurate reflection of the current threat landscape. The Digital Cyber Twin (DCT) approach offers a unique and innovative solution to support ongoing cyber risk assessment and decision-making by focusing on the present state rather than historical data.

Through the use of automation, machine reasoning, and the scientific method, the DCT approach provides a more accurate and up-to-date view of an organization's security posture. By continuously collecting and analyzing various types of data, the DCT approach allows for the identification of emerging threats and the implementation of mitigation strategies. This approach is different from traditional quantitative analysis that focuses on historical data and may not provide real-time visibility into emerging threats.

The Digital Cyber Twin (DCT) is a cutting-edge technology that combines machine reasoning, artificial intelligence, and ontology languages to provide a comprehensive view of an organization's cyber risk posture. It uses a unique approach known as argument-driven inquiry to continuously assess an organization's cyber risk and develop mitigation strategies that can be implemented. The DCT approach is designed to support continuous scientific investigation and the application of the scientific method to ongoing cyber risk assessment, providing a more accurate and up-to-date view of an organization's security posture.

The benefits of the DCT approach are numerous, including the ability to provide real-time visibility into emerging threats and vulnerabilities, the use of machine reasoning to support claims and develop Monte Carlo simulations for optimal defense strategies, and the integration of various types of data, including asset management, vulnerability assessment, threat susceptibility assessment, attack simulation, mapping, and mobilization. The transparency and explainability of the reasoning process ensure that the risk assessment process is based on empirical evidence and sound reasoning, supporting the scientific method. This type of approach is ideal for continuous threat exposure management programs. 

In this paper, we will explore the key features and benefits of the Digital Cyber Twin approach, as well as its role in supporting continuous scientific investigation and the application of the scientific method to ongoing cyber risk assessment. We will also compare the DCT approach to other risk assessment methods, such as the NIST approach and the FAIR approach, to highlight the unique advantages of the DCT approach. Finally, we will examine the challenges and limitations of the DCT approach and provide recommendations for organizations looking to implement the DCT approach to improve their cyber risk posture. 

II. The Argument-Driven Inquiry (ADI) Approach

The Argument-Driven Inquiry (ADI) approach is a method of continuous scientific investigation and the application of the scientific method to ongoing cyber risk assessment. The ADI approach is used in the Digital Cyber Twin (DCT) to provide a more accurate and up-to-date view of an organization's security posture.

In the ADI approach, claims are made about the organization's security posture based on the available evidence. The evidence is then analyzed, and reasoning is used to support or challenge the claims. If the evidence supports the claim, the claim is accepted, and if not, the claim is revised or rejected. The process is iterative, and claims are continuously tested and challenged with new evidence.

The ADI approach is beneficial in ongoing cyber risk assessment as it allows for the continuous monitoring and assessment of the organization's security posture. The approach provides real-time visibility into emerging threats and allows for the identification of potential risks before they can cause significant damage. The use of machine reasoning to support claims and develop Monte Carlo simulations ensures that the reasoning process is transparent and explainable.

Overall, the ADI approach used in the DCT supports continuous scientific investigation and the application of the scientific method, providing a more accurate and up-to-date view of an organization's security posture. It allows organizations to make informed decisions about how to optimize their security posture and reduce cyber risk.

III. The Scientific Method and the DCT

The Digital Cyber Twin (DCT) solution supports continuous scientific investigation and the application of the scientific method to ongoing cyber risk assessment. The DCT solution collects and analyzes data continuously, providing an up-to-date view of the organization's security posture. This approach is different from quantitative analysis on historical data, which may not provide an accurate reflection of the current threat landscape. By focusing on the present and incorporating automation, the DCT solution allows for the continuous monitoring and assessment of the organization's security posture, providing a more accurate and up-to-date view of the organization's risk posture.

The DCT solution uses claims, evidence, and reasoning to support the scientific method. The DCT solution collects and analyzes various types of data, including asset management data, business environment data, vulnerability assessment data, threat susceptibility assessment data, attack simulation data, mapping data, and mobilization data. By using machine reasoning to support claims and develop Monte Carlo simulations, the solution provides a transparent and explainable reasoning process, ensuring that the risk assessment process is based on sound reasoning and empirical evidence.

Here's a summary of the nine aspects of the scientific method and how they apply to the digital cyber twin approach using argument-driven inquiry:

1. Observation: The scientific method begins with making observations of a particular phenomenon. The digital cyber twin collects and analyzes data to observe and understand the cyber risk of an organization, identifying potential vulnerabilities and threats.
2. Question: Once observations are made, scientists develop questions based on those observations. The digital cyber twin uses the argument-driven inquiry approach to develop questions about the critical assets to protect, the most likely attack scenarios, and the most effective mitigation strategies.
3. Hypothesis: Scientists use observations and questions to develop hypotheses that explain the observed phenomenon. The digital cyber twin uses machine reasoning and data analytics to develop hypotheses that explain the cyber risk of an organization.
4. Prediction: Based on the hypothesis, scientists make predictions about what will happen in future experiments or observations. The digital cyber twin uses machine reasoning and Monte Carlo simulations to predict the most likely and highest-risk attack scenarios that could impact an organization.
5. Experiment: In the scientific method, experiments are designed to test the hypothesis and make predictions. In the digital cyber twin, the argument-driven inquiry approach is used to design experiments to test the claims and arguments about the critical assets to protect, the most likely attack scenarios, and the most effective mitigation strategies. We'll look at experimentation more closely when we discuss the use of automation in the DCT.
6. Data collection: The scientific method requires the collection of data to test the hypothesis and make predictions. The digital cyber twin uses a variety of tools and techniques to collect data on the assets, vulnerabilities, threats, and other factors that impact an organization's cyber risk.
7. Falsifiability: In the scientific method, a hypothesis must be testable and falsifiable. The digital cyber twin applies falsifiability by continuously testing and challenging its claims and arguments with new data, ensuring that they remain valid and accurate.
8. Objectivity: The scientific method requires objectivity in observations and experiments to eliminate bias and ensure that the results are valid and reliable. The digital cyber twin applies objectivity by using machine reasoning to analyze data and develop claims and arguments that are free from bias.
9. Peer review: Peer review is a critical component of the scientific method, ensuring that the research and results are valid and reliable. Similarly, the digital cyber twin can be subject to peer review by external auditors or security professionals to ensure that its claims and arguments are accurate and reliable.

Machine reasoning is a form of automated reasoning that uses algorithms and logical rules to make inferences based on available data. Unlike humans, machines are not susceptible to the cognitive biases that can affect human reasoning, such as confirmation bias, anchoring bias, or availability bias. Instead, machines process data in an objective and consistent manner, making logical inferences based solely on the data available.

In the case of the Digital Cyber Twin, machine reasoning is used to analyze data collected from various sources, such as asset management, vulnerability assessment, threat susceptibility assessment, attack simulation, mapping, and mobilization. The machine reasoning algorithm uses logical rules and inferences to make sense of this data, identify patterns, and develop claims and arguments based on the evidence. Since the reasoning process is based solely on the available data, it is free from the biases that may affect human reasoning.

Furthermore, the Digital Cyber Twin solution uses a transparent and explainable reasoning process, allowing for peer review and validation of the results. This further enhances the objectivity of the reasoning process, as it allows for independent experts to review the reasoning and ensure that it is free from bias.

The benefits of using the scientific method in ongoing cyber risk assessment are numerous. By incorporating the scientific method into the DCT solution, organizations can make informed decisions about how to optimize their security posture and reduce cyber risk. The scientific method ensures that the risk assessment process is based on empirical evidence and sound reasoning, allowing organizations to prioritize their mitigation efforts and optimize their security posture. Additionally, the transparency and explainability of the reasoning process support the scientific method, providing a thorough and understandable view of the organization's security posture.

The approach of using the Digital Cyber Twin (DCT) with the argument-driven inquiry (ADI) in ongoing cyber risk assessment supports several additional key aspects of the scientific method, including transparency, explainability, interpretability, reproducibility, consistency, and empirical evidence.

• Transparency: The DCT approach allows for transparency by providing a comprehensive view of the organization's assets, vulnerabilities, and threats. The use of machine reasoning to support claims and develop Monte Carlo simulations ensures that the reasoning process is explainable and transparent.
• Explainability: The DCT approach provides explainability by allowing organizations to understand the specific vulnerabilities and techniques used in attack scenarios and map them to mitigations, defense techniques, and security controls. The Digital Cyber Twin solution can use machine reasoning to develop actionable recommendations for defense teams to optimize the organization's security posture and reduce cyber risk.
• Interpretability: The DCT approach supports interpretability by providing a comprehensive view of the organization's assets, vulnerabilities, and threats. By using machine reasoning to support claims and develop Monte Carlo simulations, the solution provides a transparent and explainable reasoning process, ensuring that the risk assessment process is based on sound reasoning and empirical evidence.
• Reproducibility: The DCT approach allows for reproducibility by providing continuous monitoring and assessment of the organization's security posture. The solution collects and analyzes data continuously, allowing for the identification of emerging threats and the implementation of real-time mitigation strategies.
• Consistency: The DCT approach ensures consistency by continuously collecting and analyzing data to ensure that its claims and arguments remain valid and reliable. By using machine reasoning to support claims and develop Monte Carlo simulations, the solution provides a consistent and transparent reasoning process.
• Empirical evidence: The DCT approach relies on empirical evidence to support claims and hypotheses. The approach collects and analyzes various types of data, including asset management, vulnerability assessment, threat susceptibility assessment, attack simulation, mapping, and mobilization, to provide a comprehensive view of the organization's assets, vulnerabilities, and threats.

The DCT approach using the ADI approach supports several key aspects of the scientific method, including transparency, explainability, interpretability, reproducibility, falsifiability, consistency, objectivity, empirical evidence, and peer review. By relying on empirical evidence and sound reasoning, the DCT approach provides a comprehensive view of the organization's security posture and allows for ongoing cyber risk assessment and decision-making.

In summary, the DCT solution supports continuous scientific investigation and the application of the scientific method to ongoing cyber risk assessment. By using claims, evidence, and reasoning, the DCT solution provides a transparent and explainable reasoning process, ensuring that the risk assessment process is based on sound reasoning and empirical evidence. Incorporating the scientific method into the DCT solution allows organizations to make informed decisions about how to optimize their security posture and reduce cyber risk, providing numerous benefits for organizations.

IV. Types of Data Collected by the DCT

The Digital Cyber Twin (DCT) collects and analyzes a wide range of data to support ongoing cyber risk assessment. The following are the different types of data collected by the DCT:

Asset Management Data: The DCT collects information about IT assets in the enterprise, including servers, workstations, mobile devices, network devices, and cloud services. The system captures information about the operating system, installed applications, configuration settings, and network connectivity of each asset. The DCT uses this data to build a comprehensive view of the enterprise's assets.

Business Environment Data: The DCT collects data about the business environment by analyzing business processes, system architecture, and conducting interviews with business stakeholders. This data is used to understand the context of the assets and the potential impact of a cyber attack on the business. The DCT uses machine reasoning to analyze the data and develop a business impact analysis to identify the most critical assets to protect.

Vulnerability Assessment Data: The DCT collects vulnerability assessment data by using various tools such as vulnerability scanners, penetration testing, and manual assessments. This data is used to identify specific vulnerabilities in each IT asset. The DCT uses machine reasoning to prioritize vulnerabilities based on their severity, exploitability, and potential impact on the business.

Threat Susceptibility Assessment Data: The DCT collects threat susceptibility assessment data by using various methods such as threat intelligence feeds, penetration testing, red teaming exercises, or using a virtual attacker in the DCT. We'll discuss the use of a virtual attacker in the use of automation in the DCT section. Threat susceptibility assessment data is used to determine which specific MITRE ATT&CK Techniques are possible and not possible on each IT asset. The DCT uses machine reasoning to identify the most likely attack scenarios and to prioritize the defense of the most critical assets.

Attack Simulation Data: The DCT collects attack simulation data by using various tools and techniques, such as penetration testing, red teaming, and cyber range exercises. The attack simulation data could also come from a virtual attack that is part of the DCT. This data is used to identify all the attack scenarios targeting enterprise crown jewel assets. The DCT uses machine reasoning and Monte Carlo simulations to determine the more likely and highest-risk attack scenarios. The reasoning process is explainable and can be used to develop actionable recommendations for defense teams to optimize the organization's security posture and reduce cyber risk.

Mapping Data: The DCT collects mapping data by using various methods such as a security control matrix, MITRE D3fend techniques, and vulnerability remediation plans. This data is used to map the specific vulnerabilities and techniques used in attack scenarios to mitigations, defense techniques, and security controls. The DCT uses machine reasoning to develop actionable recommendations for defense teams to optimize the organization's security posture and reduce cyber risk.

Mobilization Data: The DCT sends data to various teams such as threat intelligence, threat detection and response, and treatments and security posture optimization teams. The teams use the data to optimize threat intelligence, prioritize the mitigation of vulnerabilities, break the kill chain of identified attack scenarios, prioritize detection and response plans, and enhance the organization's overall security posture.

In summary, the DCT collects and analyzes various types of data, providing a comprehensive view of the enterprise's assets and potential vulnerabilities and threats. The use of machine reasoning and ontology languages such as OWL, RDF, and Datalog rules ensures that the reasoning process is explainable, transparent, and based on empirical evidence. The different types of data collected by the DCT support ongoing cyber risk assessment and help to optimize an organization's security posture.

V. Comparison with Other Risk Assessment Approaches

The Digital Cyber Twin (DCT) approach is one of several risk assessment approaches used by organizations to assess their cybersecurity posture. Two other widely used approaches are the National Institute of Standards and Technology (NIST) approach and the Factor Analysis of Information Risk (FAIR) approach.

The NIST approach is a structured and systematic approach to risk assessment that involves identifying threats, vulnerabilities, and impacts to the organization's information and information systems. The NIST approach is widely used in government and industry, and its guidelines are often incorporated into regulatory requirements. One advantage of the NIST approach is its comprehensive and systematic evaluation of risk. However, the NIST approach is often criticized for being too prescriptive and not allowing for flexibility in implementation.

The FAIR approach is a quantitative risk assessment approach that focuses on measuring risk based on probabilities and impact. The FAIR approach is widely used in the financial industry and is becoming more popular in other sectors as well. One advantage of the FAIR approach is its ability to provide quantitative measurements of risk, which can be useful in decision-making. However, the FAIR approach is often criticized for its reliance on assumptions and the limitations of its mathematical models.

Compared to the NIST and FAIR approaches, the DCT approach is more focused on ongoing, real-time assessment of cyber risk. The DCT approach uses automation and machine reasoning to collect and analyze various types of data, including asset management data, business environment data, vulnerability assessment data, threat susceptibility assessment data, attack simulation data, mapping data, and mobilization data. The DCT approach is designed to support continuous risk assessment and decision-making, allowing organizations to respond to emerging threats and vulnerabilities in real-time. One advantage of the DCT approach is its ability to provide an accurate and up-to-date view of the organization's security posture. However, the DCT approach is still a relatively new approach and is not yet widely adopted, and there may be some limitations in its effectiveness in certain scenarios.

In summary, each risk assessment approach has its own strengths and weaknesses, and the choice of which approach to use will depend on the specific needs and goals of the organization. While the NIST and FAIR approaches are well-established and widely used, the DCT approach provides a more real-time and comprehensive view of an organization's security posture, which can be beneficial in today's fast-paced and rapidly evolving cyber threat landscape.

VI. Use of Automation in the DCT

The Digital Cyber Twin (DCT) approach leverages automation to support ongoing cyber risk assessment. Machine reasoning and Monte Carlo simulations play a key role in the experimentation stage of the scientific method. The DCT solution collects and analyzes a wide range of data, including asset management, vulnerability assessment, threat susceptibility assessment, attack simulation, mapping, and mobilization data, using a variety of tools and techniques. Machine reasoning is then used to develop hypotheses about the most critical assets to protect, the most likely and highest-risk attack scenarios, and the most effective mitigation strategies.

To build a virtual attacker, we need to use machine reasoning to analyze the semantics of cyber threats and IT systems. Semantic knowledge graphs are used to represent concepts and relationships in a way that is understandable to machines. These graphs enable reasoning systems to understand the meaning of the data and draw conclusions by analyzing the graph of concepts and projecting them onto new data.

In the world of cybersecurity, a semantic graph for cyber threats can be produced by using information and concepts found in standard information sources, such as the MITRE ATT&CK and NVD CVE. Attack techniques can be analyzed to define the "requirements" of the attackers. If you combine a semantic graph of cyber threats with a graph describing features of an organization's IT systems, the reasoning system can deduce what information is needed to enable the technique and build a virtual attacker that can explain how, in principle, to attack an organization.

The semantic graph is based on the Resource Description Framework (RDF), which is a directed graph described as triplets. Each triplet in an RDF graph has three components: a node for the subject, an arc with the predicate linking the subject to the object, and a node for the object. For example, the concept of a user account can be represented as a triplet: (User Account, has, Username).

To create a virtual attacker, the reasoning system finds which attack methods are relevant to an organization by checking the prerequisites for attack techniques and calculating which of them are most relevant to the organization. The more accurate information there is about the organization, the more relevant the answer will be, and you can find out which attack techniques the organization is sensitive to.

For example, a basic prerequisite for an SQL injection attack is that the system must include an SQL database. Another example is that a condition that must be met in attacks against passwords (brute force) is the use of weak passwords that can be cracked. Many MITRE ATT&CK TTPs may be irrelevant due to missing organizational prerequisites.

There are three major challenges in building a virtual attacker. The first is the precise semantic analysis of attack techniques, such as those described in MITRE ATT&CK, which are described for human understanding and not suitable for reasoning systems. The solution is to rewrite the techniques precisely with consistent and precise basic concepts and create an appropriate semantic model.

The second challenge is to create a language (ontology) that connects concepts from different attack domains, such as permissions, vulnerabilities, and configurations, and to create the semantic graph. There are detailed ontologies that explain the relationship between various cyber concepts such as the UCO of the University of Maryland or MITRE D3F3ND.

The third challenge is collecting relevant information from an organization's systems. This can be done by interfacing with existing systems and translating the information into the common language or by a dedicated scanner.

Once the system has gathered all the information, it can simulate millions of cyber attacks to determine specific attack scenarios against the organization, and calculate the risk from these attacks. The goal is to determine courses of action to mitigate attack scenarios, reduce risk and build cyber resilience.

Monte Carlo simulations are used in the experimentation stage of the scientific method to test and validate these hypotheses. The simulations enable the Digital Cyber Twin solution to develop actionable recommendations for defense teams to optimize the organization's security posture and reduce cyber risk. This approach allows for the continuous updating of data and the analysis of new threats and vulnerabilities as they arise, providing real-time visibility into emerging threats.

Monte Carlo simulations are a type of computational experiment that uses random sampling to obtain numerical solutions to complex problems. In the context of cyber risk management, Monte Carlo simulations are used to model various attack scenarios and calculate the probability of each scenario occurring. This approach is useful because it allows for the modeling of multiple variables and interactions between these variables, which can be difficult to do with traditional mathematical models.

To perform experiments using claims, evidence, and reasoning with Monte Carlo simulations, the following steps can be taken:

• Claims: Identify the claim that you want to test. In the context of cyber risk management, the claim may be that the organization's security posture is effective at mitigating cyber risk.
• Evidence: Collect the evidence that supports or refutes the claim. This may include data from asset management, vulnerability assessment, threat susceptibility assessment, and attack simulation. The evidence can be organized into different scenarios that reflect different attack vectors and potential vulnerabilities.
• Reasoning: Use machine reasoning and Monte Carlo simulations to develop a quantitative model that reflects the evidence and the claim. The model should incorporate all of the relevant variables and interactions between these variables. For example, the model may include the probability of a specific vulnerability being exploited, the effectiveness of a particular security control, and the impact of an attack on the organization's business operations.
• Experiments: Run the Monte Carlo simulations to test the model. This involves generating random samples for each variable and running the model to calculate the probability of the claim being true or false. The simulations may be run multiple times to account for different scenarios and to generate a range of probabilities.
• Analysis: Analyze the results of the simulations to determine the probability of the claim being true or false. The analysis should consider the different scenarios and the range of probabilities generated by the simulations.
• Conclusion: Draw a conclusion based on the analysis. The conclusion should be based on the evidence, reasoning, and the results of the simulations. If the simulations suggest that the claim is true, then the organization can have confidence in the effectiveness of its security posture. If the simulations suggest that the claim is false, then the organization may need to revise its security posture to address the identified vulnerabilities and risks.

In summary, Monte Carlo simulations are a useful tool for performing experiments using claims, evidence, and reasoning in the context of cyber risk management. The simulations allow for the modeling of complex interactions between variables and the quantification of probabilities, providing a more accurate and robust way to assess cyber risk.

The use of automation in the DCT provides several benefits. First, it enables the collection and analysis of data in real-time, providing a more accurate and up-to-date view of the organization's security posture. Second, it reduces the time and effort required to perform risk assessments, allowing organizations to focus their resources on mitigation strategies. Finally, it allows for the development of actionable recommendations based on empirical evidence and sound reasoning.

In summary, the use of automation in the DCT solution supports ongoing cyber risk assessment by collecting and analyzing a wide range of data, using machine reasoning to develop hypotheses, and Monte Carlo simulations to validate these hypotheses. The use of automation provides several benefits, including real-time visibility into emerging threats, reduced time and effort required to perform risk assessments, and the development of actionable recommendations based on empirical evidence and sound reasoning.

VII. Challenges and Limitations of the DCT Approach

The Digital Cyber Twin (DCT) approach offers numerous benefits for ongoing cyber risk assessment, including real-time visibility into emerging threats and the ability to prioritize mitigation efforts based on the potential impact on the organization. However, there are also several challenges and limitations that need to be considered when implementing this approach.

One challenge is the need to follow through on the prioritized issues and tasks provided by the DCT to mobilized teams and to track actions in a Plan of Actions & Milestones (POA&M) or Risk Register that can be continuously updated. While the DCT provides valuable insights and recommendations, it is up to the organization to take action and implement the recommendations. This can be a complex and ongoing process that requires resources and expertise.

Another challenge is the need for skilled professionals to manage the DCT and interpret the results. The DCT is a sophisticated tool that requires experts with the knowledge and experience to interpret the results and make informed decisions about how to optimize the organization's security posture. The organization will need to have skilled professionals to manage the DCT and interpret the results, as well as teams to implement the recommendations and monitor their effectiveness. 

To address these challenges, the DCT approach provides several opportunities, such as the ability to continuously update the security posture based on real-time data and to prioritize mitigation efforts based on the potential impact on the organization. The DCT approach also allows for the integration of multiple data sources, which can provide a more comprehensive view of the organization's security posture and improve the accuracy of risk assessments.

Moreover, the DCT approach provides the opportunity for skilled professionals to provide peer review over the DCT and validate the results. This process can ensure that the DCT approach is effective and accurate and that the results are based on sound reasoning and empirical evidence. Peer review is a crucial part of the scientific method, and it can help to identify potential biases or errors in the risk assessment process.

A limitation of using DCTs in cybersecurity is that they are still a relatively new approach, and emerging commercial DCT solutions are still maturing. As with any new technology or methodology, there may be gaps in understanding, implementation, or execution that could lead to unexpected challenges or limitations. As such, organizations that adopt DCTs must be aware that the technology may continue to evolve and that the approach may require adjustments over time as it matures. Additionally, the effectiveness of DCTs may vary depending on the context, and the benefits of the approach may not be fully realized until it has been implemented and refined over time.

In summary, while the DCT approach offers many benefits for ongoing cyber risk assessment, it also presents several challenges and limitations. The need to follow through on the prioritized issues and tasks provided by the DCT, the need for skilled professionals to manage and interpret the results, and the need for peer review to validate the results are among the challenges that need to be considered. 

VII. Supporting the 7 Interrelated Core Themes of Cybersecurity Science

The Digital Cyber Twin (DCT) approach and its use of the scientific method in ongoing cyber risk assessment support the 7 core themes of cybersecurity science in the following ways:

1. Common Language: The DCT approach supports the common language theme by using OWL ontologies, which provides a shared vocabulary for the different teams involved in the risk assessment process. The approach also uses claims, evidence, and reasoning to support the development of hypotheses, which are then tested through experimentation using Monte Carlo simulations. This process ensures that all teams involved have a consistent understanding of the language used in the risk assessment process.
2. Core Principles: The DCT approach supports the core principles theme by applying the scientific method to ongoing cyber risk assessment. Claims, evidence, and reasoning are used to develop hypotheses and to test these hypotheses through experimentation using Monte Carlo simulations. This process ensures that security concepts are well defined and understood, enabling better-informed decisions.
3. Attack Analysis: The DCT approach supports the attack analysis theme by using machine reasoning to analyze data and develop hypotheses from an attacker's perspective. Claims, evidence, and reasoning are used to develop these hypotheses and to test them through experimentation using Monte Carlo simulations. This process helps to inform security from an attacker's perspective, ensuring that the organization is better prepared to defend against cyber attacks.
4. Measurable Security: The DCT approach supports the measurable security theme by using machine reasoning to continuously assess an organization's security posture and provide a risk score that is continuously updated in real-time. This process enables the organization to measure its security posture and to make better-informed decisions about security investments and trade-offs between different security options.
5. Risk: The DCT approach supports the risk theme by providing a comprehensive and up-to-date view of an organization's security posture. The approach uses automation and machine reasoning to continuously collect and analyze various types of data from across the organization, allowing for a more effective way of mitigating cyber risk as it allows for a more complete picture of the vulnerabilities and threats facing the organization.
6. Agility: The DCT approach supports the agility theme by providing real-time monitoring and assessment of an organization's security posture. The approach uses machine reasoning and Monte Carlo simulations to develop effective mitigation strategies in real-time, allowing the organization to respond quickly and effectively to emerging threats.
7. Human Factors: The DCT approach supports the human factors theme by using machine reasoning to analyze data and develop claims and arguments that are free from bias. This process ensures that the risk assessment process is based on empirical evidence and sound reasoning, providing a more effective way to address the intangible benefits of cybersecurity and incentivizing secure behavior.

In summary, the DCT approach and its use of the scientific method support the 7 core themes of cybersecurity science by providing a more comprehensive, accurate, and up-to-date view of an organization's security posture. The approach uses machine reasoning to continuously collect and analyze various types of data from across the organization, allowing for a more effective way of mitigating cyber risk. By providing a shared vocabulary, well-defined security concepts, and a better understanding of the attacker's perspective, the DCT approach helps organizations make better-informed decisions and respond quickly to emerging threats.

IX. Conclusion

In conclusion, the Digital Cyber Twin (DCT) approach provides a unique and innovative way to support ongoing cyber risk assessment and decision-making in the field of cybersecurity. By using automation and machine reasoning, the DCT approach provides a comprehensive and up-to-date view of an organization's security posture, allowing for the identification of emerging threats and the implementation of real-time mitigation strategies.

The use of the scientific method and the application of the Argument-Driven Inquiry (ADI) approach in the DCT further support the reliability and validity of the risk assessment process. Claims, evidence, and reasoning are used to support the development of hypotheses and the testing of these hypotheses through experimentation using Monte Carlo simulations. The results of the DCT can then be used to develop actionable recommendations for defense teams to optimize an organization's security posture and reduce cyber risk.

The DCT approach also provides significant benefits over other risk assessment approaches such as NIST and FAIR. By focusing on the 'present' and incorporating automation and machine reasoning, the DCT approach provides real-time visibility into emerging threats and a more accurate and up-to-date view of an organization's security posture. This is in contrast to other approaches that may focus on 'historical' data, which may not provide an accurate reflection of the current threat landscape.

While there are certainly challenges and limitations associated with the use of the DCT approach, these can be mitigated with the right resources and expertise. The need for skilled professionals to manage the DCT and interpret the results, as well as teams to implement the recommendations and monitor their effectiveness, is essential for the success of the approach.

In the future, the DCT approach has the potential to revolutionize the field of cybersecurity, providing organizations with a more effective and efficient way to assess and manage cyber risk. With ongoing advancements in automation and machine reasoning, the DCT approach will continue to evolve, providing even more accurate and comprehensive risk assessment capabilities. As such, the DCT approach presents a unique differentiator in the field of cybersecurity and offers a promising future for organizations seeking to optimize their security posture and reduce cyber risk.