An options approach to cybersecurity investment

Cybersecurity has become a key factor that determines the success or failure of companies that rely on information systems. Therefore, investment in cybersecurity is an important financial and operational decision. Typical information technology investments aim to create value, whereas cybersecurity investments aim to minimize loss incurred by cyber attacks. Admittedly, cybersecurity investment has become an increasingly complex one, since information systems are typically subject to frequent attacks, whose arrival and impact fluctuate stochastically. Furthermore, cybersecurity measures and improvements, such as patches, become available at random points in time making investment decisions even more challenging. We propose and develop an analytical real options framework that incorporates major components relevant to cybersecurity practice, and analyze how optimal cybersecurity investment decisions perform for a private firm. The novelty of this paper is that it provides analytical solutions that lend themselves to intuitive interpretations regarding the effect of timing and cybersecurity risk on investment behavior using real options theory. Such aspects are frequently not implemented within economic models that support policy initiatives. However, if these are not properly understood, security controls will not be properly set resulting in a dynamic inefficiency reflected in cycles of over or under investment, and, in turn, increased cybersecurity risk following corrective policy actions. Results indicate that greater uncertainty over the cost of cybersecurity attacks raises the value of an embedded option to invest in cybersecurity. This increases the incentive to suspend operations temporarily in order to install a cybersecurity patch that will make the firm more resilient to cybersecurity breaches. Similarly, greater likelihood associated with the availability of a cybersecurity patch increases the value of the option to invest in cybersecurity. However, the absence of an embedded investment option increases the incentive to delay the permanent abandonment of the company’s operation due to the irreversible nature of the decision

Cyber Risk Assessment and Optimization: A Small Business Case Study

Assessing and controlling cyber risk is the cornerstone of information security management, but also a formidable challenge for organisations due to the uncertainties associated with attacks, the resulting risk exposure, and the availability of scarce resources for investment in mitigation measures. In this paper, we propose a cybersecurity decision-support framework, called CENSOR, for optimal cyber security investment. CENSOR accounts for the serial nature of a cyber attack, the uncertainty in the time required to exploit a vulnerability, and the optimisation of mitigation measures in the presence of a limited budget. First, we evaluate the cost that an organisation incurs due to a cyber security breach that progresses in stages and derive an analytical expression for the distribution of the present value of the cost. Second, we adopt a Set Covering and a Knapsack formulation to derive and compare optimal strategies for investment in mitigation measures. Third, we validate CENSOR via a case study of a small business (SB) based on: (i) the 2020 Common Weakness Enumeration (CWE) top 25 most dangerous software weaknesses; and (ii) the Center for Internet Security (CIS) Controls. Specifically, we demonstrate how the Knapsack formulation provides solutions that are both more affordable and entail lower risk compared to those of the Set Covering formulation. Interestingly, our results confirm that investing more in cybersecurity does not necessarily lead to an analogous cyber risk reduction, which indicates that the latter decelerates beyond a certain point of security investment intensity

Escaping the Echo: Understanding the Impact of Social Media on Overconcentration of Emerging Technology Security Threats

Social media platforms prioritize sensational or trending content, often overshadowing less popular but important topics and hindering discourse diversification. They evolve into echo chambers, where users predominantly encounter views aligned with their own. Security threat awareness for emerging technologies remains restricted, primarily because of the overconcentration of discussions influenced by both human and algorithmic factors. We seek to identify security threats related to emerging technology that are overshadowed and underrepresented due to the overconcentration of others. Next, we study uncertainty reduction approaches and emotional appraisal dimensions to understand how they contribute to the amplification or overconcentration of specific security threats. By combining computational NLP techniques to detect overconcentrated topics with scenario-based factorial surveys, this study proposes to provide a thorough examination of threat amplification in the realm of social media

Seizing new possibilities for expanding the scope of Cybersecurity Research in Information Systems

As Cybersecurity continues to have a significant impact on modern society, there is a pressing need for a more comprehensive research agenda in Information Systems (IS). In this study, we conducted a thorough literature review of prominent IS journals to identify gaps in Cybersecurity research practices. Our findings indicate that there is a significant gap between research and practice, particularly in terms of focus on Cybersecurity behavioural factors in the past decade. To address this gap, we recommend that future Cybersecurity research in IS should adopt a broader perspective that incorporates relevant sociotechnical knowledge areas and theories. We provide an example of Cybersecurity research topics that go beyond behavioural aspects and suggest mapping of Cybersecurity sociotechnical research knowledge areas in Information Systems to guide future research efforts. This study highlights the importance of broadening the scope of Cybersecurity research in IS to address the complex Cybersecurity challenges in contemporary practice

A Quantitative Research Study on Probability Risk Assessments in Critical Infrastructure and Homeland Security

This dissertation encompassed quantitative research on probabilistic risk assessment (PRA) elements in homeland security and the impact on critical infrastructure and key resources. There are 16 crucial infrastructure sectors in homeland security that represent assets, system networks, virtual and physical environments, roads and bridges, transportation, and air travel. The design included the Bayes theorem, a process used in PRAs when determining potential or probable events, causes, outcomes, and risks. The goal is to mitigate the effects of domestic terrorism and natural and man-made disasters, respond to events related to critical infrastructure that can impact the United States, and help protect and secure natural gas pipelines and electrical grid systems. This study provides data from current risk assessment trends in PRAs that can be applied and designed in elements of homeland security and the criminal justice system to help protect critical infrastructures. The dissertation will highlight the aspects of the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP). In addition, this framework was employed to examine the criminal justice triangle, explore crime problems and emergency preparedness solutions to protect critical infrastructures, and analyze data relevant to risk assessment procedures for each critical infrastructure identified. Finally, the study addressed the drivers and gaps in research related to protecting and securing natural gas pipelines and electrical grid systems

A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely to be exploited and focus efforts towards remediating those vulnerabilities first. The goal of this research is to demonstrate that aggregating and synthesizing readily accessible, public data sources to provide personalized, automated recommendations that an organization can use to prioritize its vulnerability management strategy will offer significant improvements over what is currently realized using the Common Vulnerability Scoring System (CVSS). We provide a framework for vulnerability management specifically focused on mitigating threats using adversary criteria derived from MITRE ATT&CK. We identify the data mining steps needed to acquire, standardize, and integrate publicly available cyber intelligence data sets into a robust knowledge graph from which stakeholders can infer business logic related to known threats. We tested our approach by identifying vulnerabilities in academic and common software associated with six universities and four government facilities. Ranking policy performance was measured using the Normalized Discounted Cumulative Gain (nDCG). Our results show an average 71.5% to 91.3% improvement towards the identification of vulnerabilities likely to be targeted and exploited by cyber threat actors. The ROI of patching using our policies resulted in a savings in the range of 23.3% to 25.5% in annualized unit costs. Our results demonstrate the efficiency of creating knowledge graphs to link large data sets to facilitate semantic queries and create data-driven, flexible ranking policies. Additionally, our framework uses only open standards, making implementation and improvement feasible for cyber practitioners and academia

Assessing the effectiveness of defensive cyber operations

Enormous amounts of resources are being allocated for defensive cyber programs. The White House’s Cyber Security National Action Plan proposes a 35% increase in federal spending on cyber security during Fiscal Year 2017. Without an appropriate understanding of how well the people, processes, defenses, and risk are measured, there will naturally be unproductive tasking, inefficient spending and ineffective reporting. In 2016, the White House established the Commission on enhancing National Cybersecurity to assess the state of our nation’s cybersecurity posture. The report recognized both the difficulty and the need to develop meaningful metrics for cybersecurity in order to better secure the cyber landscape as it pertained to the broader digital ecosystem and its connection to our economy, government, and defense. The commission focused on both the private sector as well as the government and suggested the need to perfect policies, practices and technologies. Additionally, the Marine Corps University recently released research topics addressing some of the most important concerns affecting warfighters. One of the concerns was the lack of a methodology for determining the performance of Defensive Cyber Operations (DCO). Specifically addressed was a need to better understand how actions taken by network defenders facilitate network protection. Previous analysis of this topic led to a reactive and un-actionable approach which was tied to negative events such as the quantity and category of incident reports. As there is currently no framework or scorecard built to evaluate DCO as a whole effort, a methodical approach was taken to scope the problem, compare existing frameworks, develop a framework, and present a scorecard. The first phase of research required scoping exactly what is involved in DCO at the most basic level and understanding how the DoD evaluates performance. This resulted in an understanding of the actionability of metrics, the levels of warfare, and the counterbalance of cyber asymmetry. Also identified was the military doctrine for assessments, which frames evaluations in terms of Measures of Effectiveness and Measures of Performance and supports continuous assessments that provide actionable information to decision makers. The second phase required a detailed analysis of existing frameworks that measured related functions of cybersecurity. Specifically utilized were industry accepted compliance, incident handling, governance, and risk management frameworks. The outcome identified four functional areas common to most frameworks; people, processes, defenses, and risk. The third phase involved developing a framework that evaluated the four functional areas of DCO identified in the problem-framing phase, utilizing the most appropriate features of the already established frameworks. A key facet of this evaluation was that assessments should be weighed over time to demonstrate progress but also be measured against standards, peers, and the adversary. The final phase identified the continuous reporting criteria and the tangible mechanism for evaluating an organization in terms of a scorecard. The framework is not a static list of measurements but rather supports tailoring metrics to the organization’s specific requirements. The fundamentals of the framework are organized into elements, levels, categories, ends/ways, and measures. These metrics should be documented utilizing a standardized rubric that assesses the capability and performance of the metrics. The results should be reviewed and analyzed to determine trends, areas for improvement or investment and actionable information to support decision making. Additionally, a modified Delphi analysis with expert consensus validated the major concepts put forward in this paper. Overall, this research provides a comprehensive framework to evaluate the performance of Defensive Cyber Operations in terms of people, processes, defenses, and risk, filling a knowledge gap that is increasingly vital

Impact of Internal Control, Cybersecurity Risk, and Competitive Advantage on Retail Cybersecurity Budget

Retail organizations are driven to improve security posture for many reasons, including meeting financial regulation requirements, mitigating threats of data breach, and differentiating themselves within markets affected by customer perception. The problem was that little was known about how these drivers of internal control, cybersecurity risk, and competitive advantage impact retail cybersecurity budgets within the retail sector. The purpose of this quantitative nonexperimental correlational study was to describe the relationship between cybersecurity budget and drivers of internal control, cybersecurity risk, and competitive advantage among U.S.-based retail merchant organizations. Real options theory provided a foundation for explaining this decision-making process. Data were collected from a web-based survey of 66 U.S. retail merchants. Results from multiple linear regression analysis indicated a positive predictive relationship between the driver of internal control and cybersecurity budget (F = 10.369, p = .002). Analysis also resulted in a regression formula by which assessment of this predictive organizational trait may be used to forecast or benchmark expected cybersecurity budget. Retail organizations may evaluate these factors to learn how they may be contributing to inefficient cybersecurity investment strategies, and security firms and regulators may develop improved tools and education initiatives by which to address drivers of underinvestment. With this information, leaders may effect social change by optimizing security investments that lead to lower prices, improved consumer privacy, and a more stable retail economy