Quantifying Cybersecurity Investment Returns Using Risk Management Indicators

Authors

  • Hari Priya Kommineni Software Engineer, Hadiamondstar Software Solutions LLC, Fairfax, VA 22031, USA
  • Takudzwa Fadziso Institute of Lifelong Learning and Development Studies, Chinhoyi University of Technology, ZIMBABWE
  • Pavan Kumar Gade Software Developer, City National Bank, Los Angeles, CA, USA
  • Satya Surya MKLG Gudimetla Naga Venkata IAM Engineer, Wells Fargo, 2800 S Price Rd, Chandler, AZ 85286, USA
  • Aditya Manikyala Java AWS Developer, Capital One, 8058 Dominion Pkwy, Plano, TX 75024, USA

Keywords:

Cybersecurity Investment, Risk Management Indicators, Return on Investment (ROI), Vulnerability Reduction, Incident Detection, Cost Avoidance, Regulatory Compliance

Abstract

This research uses risk management indicators to quantify cybersecurity investment returns, giving firms a framework for assessing cybersecurity spending. The study uses secondary data analysis to identify critical performance measures such as vulnerability reduction rates, incident detection and response times, threat prevention cost avoidance, regulatory compliance savings, and the Risk Reduction Index. Significant cost savings result from cybersecurity efforts that prevent incidents, reduce regulatory fines, and improve operational efficiency. Increased incident response skills decrease financial and reputational risks, highlighting the need for proactive cybersecurity. Despite constraints including indirect benefit variability and the need for consistent measurements, the research has significant policy implications. The results recommend standardized cybersecurity metrics and data-sharing to improve investment evaluation accuracy and comparability. These measurements may improve decision-making, helping organizations link cybersecurity policies with commercial goals. This study emphasizes the necessity of quantifying cybersecurity investment returns to promote a data-driven strategy for cybersecurity that improves organizational resilience and sustainability against shifting threats.

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References

Allam, A. R. (2020). Integrating Convolutional Neural Networks and Reinforcement Learning for Robotics Autonomy. NEXG AI Review of America, 1(1), 101-118.

Alsaleh, M. N., Al-shaer, E., Husari, G. (2017). ROI-Driven Cyber Risk Mitigation Using Host Compliance and Network Configuration. Journal of Network and Systems Management, 25(4), 759-783. https://doi.org/10.1007/s10922-017-9428-x

Boinapalli, N. R. (2020). Digital Transformation in U.S. Industries: AI as a Catalyst for Sustainable Growth. NEXG AI Review of America, 1(1), 70-84.

Harrison, S., Jürjens, J. (2018). Data Security and Consumer Trust in FinTech Innovation in Germany. Information and Computer Security, 26(1), 109-128. https://doi.org/10.1108/ICS-06-2017-0039

Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95

Kong, H-k., Kim, T-s., Kim, J. (2012). An Analysis on Effects of Information Security Investments: A BSC Perspective. Journal of Intelligent Manufacturing, 23(4), 941-953. https://doi.org/10.1007/s10845-010-0402-7

Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663

Kundavaram, R. R., Rahman, K., Devarapu, K., Narsina, D., Kamisetty, A., Gummadi, J. C. S., Talla, R. R., Onteddu, A. R., & Kothapalli, S. (2018). Predictive Analytics and Generative AI for Optimizing Cervical and Breast Cancer Outcomes: A Data-Centric Approach. ABC Research Alert, 6(3), 214-223. https://doi.org/10.18034/ra.v6i3.672

Lis, P., Mendel, J. (2019). Cyberattacks on Critical Infrastructure: An Economic Perspective 1. Economics and Business Review, 5(2), 24-47. https://doi.org/10.18559/ebr.2019.2.2

Markovic-Petrovic, J. D., Stojanovic, M. D., Rakas, S. V. B. (2019). A Fuzzy AHP Approach for Security Risk Assessment in SCADA Networks. Advances in Electrical and Computer Engineering, 19(3), 69-74. https://doi.org/10.4316/AECE.2019.03008

Riesco, R., Villagrá, V. A. (2019). Leveraging Cyber Threat Intelligence for a Dynamic Risk Framework. International Journal of Information Security, 18(6), 715-739. https://doi.org/10.1007/s10207-019-00433-2

Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151

Rodriguez, M., Sridharlakshmi, N. R. B., Boinapalli, N. R., Allam, A. R., & Devarapu, K. (2020). Applying Convolutional Neural Networks for IoT Image Recognition. International Journal of Reciprocal Symmetry and Theoretical Physics, 7, 32-43. https://upright.pub/index.php/ijrstp/article/view/158

Schatz, D., Bashroush, R. (2017). Economic Valuation for Information Security Investment: A Systematic Literature Review. Information Systems Frontiers, 19(5), 1205-1228. https://doi.org/10.1007/s10796-016-9648-8

Schatz, D., Bashroush, R. (2018). A Structural Model Approach for Assessing Information Security Value in Organizations. International Journal of Strategic Decision Sciences, 9(4), 47-69. https://doi.org/10.4018/IJSDS.2018100104

Sharma, S., Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Literature Review. Risks, 7(1), 29. https://doi.org/10.3390/risks7010029

Sikula, N. R., Mancillas, J. W., Linkov, I., Mcdonagh, J. A. (2015). Risk Management is Not Enough: A Conceptual Model for Resilience and Adaptation-based Vulnerability Assessments. Environment Systems & Decisions, 35(2), 219-228. https://doi.org/10.1007/s10669-015-9552-7

Published

2020-12-31

How to Cite

Kommineni, H. P., Fadziso, T., Gade, P. K., Venkata, S. S. M. G. N., & Manikyala, A. (2020). Quantifying Cybersecurity Investment Returns Using Risk Management Indicators. Asian Accounting and Auditing Advancement, 11(1), 117–128. Retrieved from https://4ajournal.com/article/view/97

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