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Cyber risk scoring
June 16, 2023
Contents
Table 1: Comparison of Security Solutions. 15
Abstract
Due to the ever-changing nature of the cyber threat environment, cyber risk management has emerged as a top priority for businesses throughout the globe. Organizations depend on cyber risk scoring systems to measure and estimate the possible impact and probability of cyber events in order to successfully manage cyber risk. In order to shed light on the merits, shortcomings, and potential future developments of cyber risk scoring systems, this review research analyzes their history, methodology, and performance. The analysis begins out with a brief history of cyber risk scoring, detailing its evolution from intuitive methods to those grounded on hard facts. Inconsistent risk ratings were a consequence of earlier methods’ reliance on expert opinion and qualitative evaluations. Quantitative models and frameworks, such as the FAIR model, have, nevertheless, brought about a revolutionary shift in the sector by offering a standardized and systematic method for measuring cyber risk. These approaches use metrics like threat frequency, susceptibility, and monetary effect to provide a more quantitative evaluation of risk. Additionally, cyber risk scoring procedures have been revolutionized due to the use of machine learning and artificial intelligence. With the use of machine learning algorithms, cyber risk patterns and trends may be analyzed and identified from massive volumes of data. This improves risk score models’ precision and foresight, letting businesses protect themselves against dangers in advance. There is considerable potential for cyber risk assessment systems to benefit from the use of AI approaches. A methodical process was used to identify potentially useful sources of data for the evaluation. Primary materials included books, articles, white papers, and reports from universities and market research firms. To guarantee a thorough examination of the issue, a wide variety of sources were considered and ultimately chosen, including commercial enterprises, NGOs, academics, specialists, and government bodies. In the literature study, we compare and contrast several cyber risk score systems, classifying them into broad categories according to the methods and techniques they use. The benefits and drawbacks of each approach are covered, along with their respective contributions to the field. To fully grasp the answers, we look at important factors including methodology, data inputs, scoring systems, and applicability. Insightful conclusions may be drawn by comparing the various approaches used. It is now clear that the precision and neutrality of risk scores greatly depend on the methods used to generate them. While machine learning algorithms give the possibility of automated analysis and continual learning, quantitative models offer a defined and quantified method. Each company has its own set of requirements and limitations that must be taken into account when deciding on the best approach to take. Cyber risk score also heavily relies on the accessibility and quality of data sources. We can learn a lot by looking at attack records, vulnerability databases, and threat intelligence feeds. However, obstacles including poor data quality, lack of accessibility, and outdated information must be overcome to guarantee accurate risk evaluations. This review study’s results shed light on various voids and opportunities for enhancing cyber risk rating. If measures and criteria were standardized across sectors, risk ratings might be more easily compared and benchmarked. The responsiveness and agility of risk scoring systems may be improved by incorporating real-time data and dynamic elements, such as new threats and changing vulnerabilities. Further, more personalized and relevant risk rating would be possible with the incorporation of context-specific criteria like industry-specific legislation and compliance needs.
Introduction
Organizations in today’s highly digitized and linked world confront a growing variety of cyber threats and vulnerabilities. In order to analyze and quantify the risks posed by these threats, cyber risk scoring has developed as a useful tool. The goal of this literature review is to learn more about cyber risk scoring, including its history, definitions, goals, systematic methodology, and conclusions.
Background
The increasing complexity of cyber-attacks and the rising dependence on digital systems have contributed to a dramatic shift in the cybersecurity environment in recent years. The ever-changing and intricate nature of cyber hazards makes it difficult to manage them using the tried-and-true methods of qualitative risk assessments and compliance-based frameworks. Therefore, the idea of cyber risk scoring has gained traction as a tool to give businesses with a more quantitative and uniform method of evaluating their exposure to cyber threats.
Prior and ongoing research on cyber risk scoring has centered on the creation of tools for quantifying and assessing these threats. The NIST Cybersecurity Framework, the PCI Data Security Standard, and the Cybersecurity Maturity Model Certification are just a few examples of the many industry standards and frameworks that include cyber risk score into their methodologies. Probabilistic models, machine learning methods, and risk-based scoring systems are just some of the methods that have been investigated in the classroom and the lab.
We have Defined important phrases associated with cyber risk score in order to ensure everyone is on the same page. Data breaches, illegal access, virus attacks, and system failures are all examples of cyber risks that might result in injury or damage. To quantify the severity, probability, and possible effect of a risk, a process known as “risk scoring” is used. Thus, to score cyber hazards is to measure and evaluate them using established criteria and metrics.
The goal of this literature review is to synthesize and organize the current body of research on cyber risk rating. The purpose of this paper is to identify the strengths, limits, and possible areas for development in cyber risk scoring systems by synthesizing and assessing the relevant literature, frameworks, and models. The hope is that this work will shed light on cyber risk rating procedures that businesses of all stripes may put to good use.
The objectives of this review research required a methodical strategy, which was implemented here. This required an exhaustive search through journal articles, company reports, and applicable frameworks. Key topics and categories were used to evaluate, synthesize, and arrange the data collected. Different cyber risk score systems were compared and contrasted, similarities were identified, and variances in approaches were investigated as part of the evaluation process.
This review study’s results showed that there is a wide variety of cyber risk assessment approaches and models. Methods based on qualitative evaluations often rely on subjective criteria and the opinion of experts in the field. Some researchers prefer quantitative approaches, such as those based on mathematics, computers, and statistical analysis. Cyber risk is often measured in terms of four dimensions: threat probability, vulnerability seriousness, potential impact, and resilience capacities. Cyber risk scoring has advantages like better risk prioritizing and decision making, but it also has drawbacks like inconsistent data and inaccurate models.
Review Method
Methods for collecting, analyzing, and synthesizing data for the cyber risk score research are described here. The review methodology, including the selection criteria for information sources, the kind of information gathered, and the particular procedures applied for synthesis, comparison, and analysis, are described in detail in the following paragraphs.
Criteria for Choosing Information Sources A methodical procedure was used to pick relevant information sources to guarantee a thorough examination. The following factors were considered while choosing the sources utilized for this study:
- Scholarly Papers: Scholarly articles on cyber risk scoring might be found in peer-reviewed academic publications. We looked for appropriate publications using search engines like Google Scholar and databases like IEEE Xplore and the ACM Digital Library. Academic publications were chosen based on their appropriateness to the issue at hand, the quality of their research methods, and how recently they were published. Priority was given to papers that shed light on new approaches to cyber risk assessment, whether via empirical research or theoretical frameworks.
Cyber risk score trends and practical insights may be gleaned from reports and publications produced by credible industry groups, cybersecurity firms, and government agencies. b. Industry publications were chosen based on factors such as the authority of the cited source, the freshness of the data, and its relevance to the study’s subject. Particular attention was paid to reports that included case studies, best practices, and experiences with actual implementation.
- Frameworks and Standards: Key information sources were established frameworks and standards linked to cyber risk score, such as the NIST Cybersecurity Framework, PCI DSS, and CMMC. The selection criteria for these resources were their popularity, usefulness, and potential to affect standard practice in the field. Understanding the present status of the sector requires a familiarity with relevant frameworks and standards that give guidelines, procedures, or metrics for cyber risk assessment.
Extracting Useful Data:
Data and insights were gleaned from the chosen sources to back up the synthesis, comparison, and analysis presented in the literature review and debate. The following categories of data were collected:
Information about cyber risk scoring approaches, models, and frameworks was culled from scholarly publications, business reports, and other sources. a. Details on their theoretical foundations, data requirements, grading procedures, and practical applications were provided. Methodologies’ fundamental components, such risk assessment strategies, risk calculation procedures, and risk aggregation methods, were recorded.
Cyber risk scoring’s primary measurements and criteria were gleaned from scholarly articles, business reports, and theoretical frameworks. b. Threat probability, vulnerability severity, impact potential, and organizational resilience were all included in these measurements. Metrics were chosen based on their usefulness for cyber risk scoring, their precision in measuring danger, and the flexibility with which they might be implemented.
In order to demonstrate the usefulness of cyber risk scoring in a variety of settings, real-world case studies, practical examples, and implementation experiences were culled from various sources, including industry reports and scholarly articles. These examples illuminated the benefits, difficulties, and takeaways of implementing cyber risk scoring techniques. Relevance to the issue, availability of information, and diversity in industry and organization size were prioritized in selecting case studies and examples.
Definition of Review Approach:
The purpose of this review was to give the reader with more refined knowledge via a process of synthesis, analysis, and comparison. The following are examples of the approaches used:
- Synthesis Method: The collected data was synthesized by locating commonalities, trends, and patterns across cyber risk rating approaches. Methodology kinds, risk assessment methods, scoring mechanisms, and metrics were employed to classify the gleaned data. By collecting data from several sources, a thorough analysis of the topic was produced, one that brought to light the most important features and variances of cyber risk score.
Similarities and discrepancies between the approaches, models, and frameworks gleaned from the information sources were identified via a comparative study. The purpose of this study was to shed light on the advantages and disadvantages of existing methods of cyber risk assessment and to spot new trends and areas of agreement. The purpose of this study was to create a more nuanced knowledge of the topic by comparing different approaches and models.
The retrieved data was analyzed critically to see if the cyber risk score approaches were useful, applicable, and feasible. Methods were compared and contrasted, implementation difficulties and constraints were evaluated, and opportunities for further study were identified. The purpose of the study was to give a fair evaluation of cyber risk scoring as it is now, including its merits and opportunities for improvement.
The review approach used in this investigation was an integrated strategy of exhaustive source selection, exhaustive data extraction, and meticulous synthesis, comparison, and analysis. The purpose of this study was to contribute to the current body of knowledge and to guide future research and practice in the area of cyber risk assessment by taking this approach.
Literature Review
In this evaluation of the research available on cyber security awareness, knowledge, behavior, and risk assessment, we look into a broad variety of studies and research publications. Topics such as cyber risk categorization, cyber risk assessment frameworks, and risk analysis approaches are covered in the chosen materials, providing invaluable insights into the subject. This section tries to offer a thorough overview of the present status of research in these domains by assessing and synthesizing the available solutions.
2.1 Cyber Security Awareness, Knowledge, and Behavior
Protecting individuals, businesses, and society against cyber risks depends heavily on people’s awareness, understanding, and conduct in this area. The degrees of cyber security awareness, knowledge, and behavior were compared in research by Zwilling et al. (2022). Understanding people’s awareness and knowledge is crucial for enhancing cyber security behavior, as their study showed. The research elucidated the elements such as education, age, and technical literacy that influence user behavior by evaluating data from numerous nations. It stressed the need of focused interventions such awareness campaigns and instructional programs to raise consumers’ general level of cyber security understanding.
2.2 Risk Analysis and Cyber Operations
Proactively detecting and mitigating cyber threats requires efficient cyber security operations and risk assessment. A user-centric machine learning framework for cyber security operations centers was suggested by Feng, Wu, and Liu (2017). Their plan was to use machine learning to make these facilities more efficient. The framework’s goal was to enhance incident response capabilities and to promote proactive threat identification and mitigation by combining user-centric elements like user behavior analytics and contextual information. This study emphasizes the significance of user-centric methods to tackling increasing cyber threats and demonstrates the potential of machine learning in enhancing cyber security operations.
2.3 Cyber Threat Origins, Consequences, and National-Level Risk Assessment
Understanding the origins and consequences of cyber risks is crucial for developing effective risk assessment and management strategies. After investigating the origins and outcomes of cyber risks, Crotty and Daniel (2022) concluded that a comprehensive evaluation of cyber risk requires a mixture of qualitative and quantitative methods. Possible motivations for cyber attacks were investigated, such as financial gain, political purposes, and espionage. The research used case studies and an analysis of the impact of cyber threats on businesses, governments, and individuals to stress the need of comprehending the multifaceted nature of cyber dangers. It highlighted the need of analyzing cyber risks from several angles, such as threat intelligence and incident response.
Janiszewski, Felkner, and Lewandowski (2019) propose a vulnerability management and threat intelligence–based national cyber risk assessment. The researchers set out to develop a method for systematically assessing and managing cyber hazards on a worldwide scale in their investigations. Using vulnerability management techniques and aggregating threat information from many sources including open-source intelligence and dark web monitoring, their method intends to develop a complete framework for identifying and addressing cyber threats at the national level. Improve national cyber security and deepen our understanding of cyber risk assessment with the help of this research.
2.4 Classification and Evaluation Methodologies for Cyber Risk
The proper prioritization and allocation of resources relies on a thorough assessment and classification of cyber risks. (Sheehan, Murphy, Mullins, and Ryan, 2019) devised a cyber-risk classification system for CAVs. The framework was created so that particular cyber hazards associated with progress in transportation technology may be categorized and evaluated. Their findings laid the groundwork for the creation of efficient risk management techniques in the transportation industry by highlighting issues including vehicle connection, data privacy, and remote hacking risks.
In addition, the Vercasm-cps methodology for vulnerability analysis and cyber risk assessment in cyber-physical systems (CPS) was implemented by Northern, Burks, Hatcher, Rogers, and Uly. The researchers looked at the potential dangers of combining cyber and physical systems in CPS and how to lessen such dangers. Their study aims to shed light on efficient risk assessment approaches for safeguarding critical infrastructures by assessing the vulnerabilities of systems, prospective attack paths, and the effects of cyber-physical assaults.
In the field of third-party risk management, Keskin et al. (2021) compared several kinds of risk rating reports that don’t pry too closely. Their research aims to examine and contrast several methods for analyzing and controlling cyber threats posed by collaborations with third parties. The study’s overarching goal was to help businesses make more informed decisions about the risk assessment methods they should use when working with outside parties by comparing and contrasting different risk score approaches.
Using a quantitative bow-tie model, Sheehan, Murphy, Kia, and Kiely (2021) provided a methodology for classifying and assessing cyber risks. The study’s authors hoped the bow-tie strategy would give a systematic way to examine and control cyber hazards. possible cyber dangers could be seen, preventative steps could be identified, and the possible effects could be evaluated thanks to the framework. The findings of this study provide businesses with a methodical and quantitative means of dealing with cyber dangers.
2.5 Cyber Risk Analysis Techniques
Cloud provider settings are studied by Akinrolabu, Nurse, Martin, and New (2019) as they investigate cyber risk assessment models. Their research focused on existing methods and upcoming requirements for handling cyber hazards in the cloud. The study’s goal was to shed light on the difficulties and potential benefits of cyber risk assessment in cloud-based settings by analyzing current frameworks and approaches. This study enlightens the necessity for individualized risk assessment strategies in cloud provider environments and adds to our knowledge of cloud security.
A multicriteria decision framework for managing cybersecurity risks was provided by Ganin et al. (2020). Through their studies, they hoped to provide businesses a unified framework for assessing cybersecurity threats and assigning priorities. The methodology improved cyber risk management decision-making by taking into account attack severity, probability, and mitigation costs.
Cybersecurity threats in the banking sector were studied by Florackis, Louca, Michaely, and Weber (2023). Their study’s overarching goal was to identify ways to reduce financial institutions’ vulnerability to cyberattacks. The research analyzed the threats and difficulties faced by financial institutions and gleaned appropriate risk management techniques to safeguard private financial information and keep the confidence of stakeholders.
In conclusion, this literature review presents a thorough analysis of several studies and research articles dealing with cyber security consciousness, information, conduct, and evaluation of risk. Cyber risk analysis methodologies, cyber risk categorization systems, and cyber risk causes and implications are only few of the many areas covered by the examined materials. The section gives useful insights into the present status of research in the subject of cyber security by combining and evaluating the results of various studies. These discoveries provide the groundwork for future research and development toward the goal of effective cyber security measures.
| Security Solution | Criteria-A | Criteria-B | Criteria-C | Criteria-D |
| Solution-1 | Emphasizes user training | Incorporates machine learning | Addresses human behavior | Provides real-time threat alerts |
| Solution-2 | Utilizes qualitative and quantitative methods | Comprehensive risk assessment | Considers potential consequences | Offers customizable risk scoring |
| Solution-3 | Tailored for connected and autonomous vehicles | Identifies specific vehicle vulnerabilities | Analyzes cyber risks in transportation technologies | Incorporates vulnerability analysis for cyber-physical systems |
| Solution-4 | Designed for cloud provider environments | Assess cloud-specific vulnerabilities | Addresses challenges in cloud computing | Provides scalable risk assessment solutions |
Table 1: Comparison of Security Solutions
Based on the findings of Zwilling et al.’s (2022) study on cyber security awareness, knowledge, and behavior, the efficacy of Solution-1 in raising users’ levels of awareness and education about the topic may be determined. Feng, Wu, and Liu’s (2017) user-centric machine learning frameworks may also be considered for incorporation into cyber security procedures.
The second solution is a discussion of the value of both qualitative and quantitative approaches to cyber risk assessment, as presented by Crotty and Daniel (2022). Solution 2 may be evaluated based on how well it integrates these methodologies, offering a full picture of cyber risks and their effects. The cyber risk assessment methodology provided by Janiszewski, Felkner, and Lewandowski (2019) at the national level may also be included.
Thirdly, a cyber-risk categorization system designed for autonomous and connected cars was proposed by Sheehan, Murphy, Mullins, and Ryan (2019). Third Solution may be judged on how well it can classify and assess cyber hazards related to developing modes of transportation. In addition, the cyber risk assessment and vulnerability analysis methods for CPS presented by Northern et al. (2021) might be included.
Akinrolabu, Nurse, Martin, and New (2019) offered a fourth solution by focusing on cyber risk assessment models in the context of cloud provider settings. Solution 4 may be evaluated according to how well it meets the demands of analyzing and managing cyber threats in cloud computing and how well it fits into the context of cloud-based security solutions.
Discussion and Conclusion
This review paper’s discussion and conclusion acts as a reflection on and summary of the results reported in the literature review. It’s a chance to discuss the major contributions of existing cyber risk scoring systems, point out any problems with the present state of affairs, and propose avenues for further study.
Cyber risk score systems have developed from subjective evaluations to more objective, data-driven ones, as the literature analysis made clear. Inconsistencies in risk rating were a common problem with early approaches since they depended so much on expert opinion and qualitative judgments. However, with the advent of quantitative models and frameworks like the FAIR model, it became possible to measure cyber risk in a systematic and standardized manner. These methods have improved businesses’ capability to assess their risk exposure and implement effective countermeasures.
Another major contributor to the development of cyber risk scoring procedures is the use of machine learning and artificial intelligence. With the help of machine learning algorithms, analysts can now sift through mountains of data in search of indicators of growing cyber threat. As a result, risk scoring algorithms are now more precise and reliable in their predictions. Cyber risk scoring systems might benefit greatly from the use of AI methods, which could make them more accurate, reliable, and convenient to use.
Several significant conclusions were drawn from the comparison of the various available systems. To begin, risk rating is only as reliable and objective as the process used to create it. While quantitative models provide for more structure and quantifiable outcomes, the opportunity for automated analysis and ongoing learning is offered by machine learning algorithms. Each company has its own set of requirements and limitations that must be taken into account when deciding on the best approach to take.
Another important factor in cyber risk score is the availability and quality of data inputs. Threat intelligence streams, vulnerability databases, and historical data on attacks are all useful resources. However, obstacles including poor data quality, lack of accessibility, and outdated information must be overcome to guarantee accurate risk evaluations.
While cyber risk scoring has come a long way, it still has several major shortcomings and room for development. To begin, there should be universal measures and criteria that may be used in any business or organization. Risk scores might thus be more easily compared to one another and benchmarked. Incorporating real-time data and taking into account dynamic elements like new threats and changing vulnerabilities may make risk scoring systems more responsive and adaptive.
The incorporation of context-specific criteria, such as legislation and compliance requirements unique to an organization’s sector, would also make risk score more personalized and applicable. To put it another way, this would let businesses prioritize their prevention efforts in accordance with their unique risk profiles and regulatory mandates.
Finally, this review article summarizes the development of cyber risk rating techniques. The research shed light on the benefits and drawbacks of various methods, stressing the significance of objective measures, trustworthy data inputs, and context-specific concerns. Based on the results, it seems that quantitative models, machine learning methods, and individualized approaches may all work together to improve the efficacy and breadth of cyber risk rating.
In the future, researchers should work to fill in the blanks and overcome the obstacles they’ve found. As part of this process, we must refine risk scoring models by including dynamic and context-specific aspects as well as standardizing measures. Organizations’ proactivity in managing cyber risks and protecting digital assets and sensitive information may be improved with further development of cyber risk scoring.
References
Zwilling, M., Klien, G., Lesjak, D., Wiechetek, Ł., Cetin, F., & Basim, H. N. (2022). Cyber security awareness, knowledge and behavior: A comparative study. Journal of Computer Information Systems, 62(1), 82-97.
Sheehan, B., Murphy, F., Mullins, M., & Ryan, C. (2019). Connected and autonomous vehicles: A cyber-risk classification framework. Transportation research part A: policy and practice, 124, 523-536.
Feng, C., Wu, S., & Liu, N. (2017, July). A user-centric machine learning framework for cyber security operations center. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 173-175). IEEE.
Crotty, J., & Daniel, E. (2022). Cyber threat: its origins and consequence and the use of qualitative and quantitative methods in cyber risk assessment. Applied Computing and Informatics, (ahead-of-print).
Janiszewski, M., Felkner, A., & Lewandowski, P. (2019). A novel approach to national-level cyber risk assessment based on vulnerability management and threat intelligence. Journal of Telecommunications and Information Technology.
Sheehan, B., Murphy, F., Mullins, M., & Ryan, C. (2019). Connected and autonomous vehicles: A cyber-risk classification framework. Transportation research part A: policy and practice, 124, 523-536.
Northern, B., Burks, T., Hatcher, M., Rogers, M., & Ulybyshev, D. (2021). Vercasm-cps: Vulnerability analysis and cyber risk assessment for cyber-physical systems. Information, 12(10), 408.
Keskin, O. F., Caramancion, K. M., Tatar, I., Raza, O., & Tatar, U. (2021). Cyber third-party risk management: A comparison of non-intrusive risk scoring reports. Electronics, 10(10), 1168.
Sheehan, B., Murphy, F., Kia, A. N., & Kiely, R. (2021). A quantitative bow-tie cyber risk classification and assessment framework. Journal of Risk Research, 24(12), 1619-1638.
Akinrolabu, O., Nurse, J. R., Martin, A., & New, S. (2019). Cyber risk assessment in cloud provider environments: Current models and future needs. Computers & Security, 87, 101600.
Ganin, A. A., Quach, P., Panwar, M., Collier, Z. A., Keisler, J. M., Marchese, D., & Linkov, I. (2020). Multicriteria decision framework for cybersecurity risk assessment and management. Risk Analysis, 40(1), 183-199.
Florackis, C., Louca, C., Michaely, R., & Weber, M. (2023). Cybersecurity risk. The Review of Financial Studies, 36(1), 351-407.
Eggers, S., & Le Blanc, K. (2021). Survey of cyber risk analysis techniques for use in the nuclear industry. Progress in Nuclear Energy, 140, 103908.
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