Adapting to the SEC’s New Cybersecurity Disclosure Requirements: Implications for Financial Reporting
Keywords:
SEC cybersecurity disclosure, financial reportingAbstract
The Securities and Exchange Commission (SEC) has introduced new cybersecurity disclosure requirements that reshape how publicly traded companies communicate their cybersecurity risks, incidents, & governance structures. These regulations are designed to enhance transparency, offering investors a clearer view of how companies address the growing threat of cyberattacks. By mandating more detailed and timely reports on cybersecurity matters, the SEC aims to ensure that investors can access critical information when making investment decisions. The new rules require companies to disclose material cybersecurity incidents promptly and to provide insights into their risk management strategies, governance frameworks, and the financial impact of cyber events. These changes represent a significant shift in corporate reporting, emphasizing the need for businesses to disclose incidents & outline their preparedness & resilience strategies. However, this shift presents challenges, particularly around the timely and accurate identification of incidents and the complexity of quantifying the financial impact of cyber risks. Companies must also need help to balance transparency with protecting sensitive business information, especially when detailing their cybersecurity strategies. As businesses work to comply with these requirements, they will likely encounter growing pains, particularly in aligning their internal practices with the new standards. These disclosures also potentially influence investor relations and corporate governance significantly. Companies can demonstrate their commitment to safeguarding investor interests & building trust by addressing cybersecurity risks more directly. This move towards more robust and transparent reporting could change how stakeholders view corporate resilience in the face of cyber threats, offering new perspectives on risk management and long-term sustainability. Ultimately, businesses must embrace these new requirements not only to comply with regulations but also as an opportunity to strengthen their cybersecurity frameworks and improve their overall governance practices, setting a strong example in an increasingly digital and interconnected world.
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Wang, T., Yen, J. C., & Yoon, K. (2022). Responses to SEC comment letters on cybersecurity disclosures: An exploratory study. International Journal of Accounting Information Systems, 46, 100567.
Daoud, M. M., & Serag, A. A. (2022). A proposed framework for studying the impact of cybersecurity on accounting information to increase trust in the financial reports in the context of industry 4.0: An event, impact and response approach. التجارة والتمويل, 42(1), 20-61.
Leiva, A. M., & Clark, M. E. (2020). COVID-19 considerations for SEC cybersecurity guidance, disclosure, enforcement, and parallel proceedings: navigating the new normal. Journal of Investment Compliance, 21(2/3), 111-126.
Young, S. (2012). Contemplating corporate disclosure obligations arising from cybersecurity breaches. J. Corp. L., 38, 659.
Peng, J., & Krivacek, G. (2020). The growing role of cybersecurity disclosures. ISACA Journal, 2020, 1-7.
Karmel, R. S. (2016). Disclosure reform—The SEC is riding off in two directions at once. The Business Lawyer, 71(3), 781-834.
Skinner, C. P. (2019). Bank disclosures of cyber exposure. Iowa L. Rev., 105, 239.
Martin, D., Engvall, D., Burke, K., Hodgkins, G., Franker, M., & Hooper, R. (2019). US SEC report calls for better internal accounting controls for cyber-related threats. Journal of Investment Compliance, 20(1), 5-9.
Bakker, T. G. (2015). Accuracy of self-disclosed cybersecurity risks of large US banks.
Fisher, R., Wood, J., Porod, C., & Greco, L. (2019). Evaluating cyber risk reporting in US financial reports. Cyber Security: A Peer-Reviewed Journal, 3(3), 275-286.
Eaton, T. V., Grenier, J. H., & Layman, D. (2019). Accounting and cybersecurity risk management. Current Issues in Auditing, 13(2), C1-C9.
Yang, L., Lau, L., & Gan, H. (2020). Investors’ perceptions of the cybersecurity risk management reporting framework. International Journal of Accounting & Information Management, 28(1), 167-183.
Jin, J. (2015). Cybersecurity disclosure effectiveness on public companies.
Harper Ho, V. (2018). Nonfinancial risk disclosure and the costs of private ordering. American Business Law Journal, 55(3), 407-474.
Rosati, P., Gogolin, F., & Lynn, T. G. (2017). Cyber-Security Incidents, External Monitoring and Probability of Restatements. External Monitoring and Probability of Restatements (July 29, 2017).
Thumburu, S. K. R. (2022). EDI and Blockchain in Supply Chain: A Security Analysis. Journal of Innovative Technologies, 5(1).
Thumburu, S. K. R. (2022). A Framework for Seamless EDI Migrations to the Cloud: Best Practices and Challenges. Innovative Engineering Sciences Journal, 2(1).
Gade, K. R. (2022). Data Analytics: Data Fabric Architecture and Its Benefits for Data Management. MZ Computing Journal, 3(2).
Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
Thumburu, S. K. R. (2021). A Framework for EDI Data Governance in Supply Chain Organizations. Innovative Computer Sciences Journal, 7(1).
Thumburu, S. K. R. (2021). EDI Migration and Legacy System Modernization: A Roadmap. Innovative Engineering Sciences Journal, 1(1).
Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50
Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70
Naresh Dulam. Data Lakes Vs Data Warehouses: What’s Right for Your Business?. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Nov. 2016, pp. 71-94
Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93
Sarbaree Mishra. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, July 2022, pp. 329-52
Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28
Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03
Sarbaree Mishra. “Reducing Points of Failure - a Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Jan. 2022, pp. 568-95
Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77