The Great Resignation: Managing Cybersecurity Risks During Workforce Transitions
DOI:
https://doi.org/10.56472/ICCSAIML25-142Keywords:
Great Resignation, Cybersecurity, Workforce Transition, Insider Threats, Risk ManagementAbstract
Where organizations used to rely on employees tenured with their company, the Great Resignation has presented new problems to organizational structure and fortification. Such a process usually leads to disrupted employee productivity, brain drain and, most importantly, increased vulnerability to cyber threats. Loyal workers, intentionally or unintentionally disloyal workers, and employees who leave the organization can compromise organizational confidential information, such as innovation, customer data, and other data that the organization considers to be highly valuable. Research has also revealed that workforce transition time is also the highest-risk activity period for insiders, wherein activities like the unauthorized download of data or accidental data leaks when offboarding an employee are likely to occur. Moreover, there are difficulties, particularly for organizations that cannot apply adequate access controls and monitoring during the notice periods, making them much more sensitive to data leaks. Further, this paper examines these paramount cybersecurity threats and offers an organized framework for their mitigation. Explores how the risks can be reduced under this through policies like strong access controls, opaque data monitoring systems, and comprehensive offboarding. To get practical recommendations for managing the problem with data sharing, the example of using machine learning to realize the mechanisms for identifying anomalies and graph-theory-based mathematical models is given. Thus, this research provides a comprehensive set of procedures to address the consequences of the Great Resignation for organizations and safeguard their assets during worker turnovers
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[1] Lewis, A. C., Danielson, J. L., Cojocaru, R. A., & Steinhoff, J. C. (2022). Turn the Great Resignation into a great opportunity. The Journal of Government Financial Management, 71(2), 18-25.
[2] Animesh Kumar, “Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)”, Transactions on Engineering and Computing Sciences, 12(4), 59-69. 2024.
[3] Sudheer Panyaram, (2025/5/18). Intelligent Manufacturing with Quantum Sensors and AI A Path to Smart Industry 5.0. International Journal of Emerging Trends in Computer Science and Information Technology. 140-147.
[4] Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-23.
[5] Kirti Vasdev. (2025). “Churn Prediction in Telecommunications Using Geospatial and Machine Learning Techniques”. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 13(1), 1–7. https://doi.org/10.5281/zenodo.14607920
[6] Ibrahim, A., Thiruvady, D., Schneider, J. G., & Abdelrazek, M. (2020). The challenges of leveraging threat intelligence to stop data breaches. Frontiers in Computer Science, 2, 36.
[7] Marella, Bhagath Chandra Chowdari, and Gopi Chand Vegineni. "Automated Eligibility and Enrollment Workflows: A Convergence of AI and Cybersecurity." AI-Enabled Sustainable Innovations in Education and Business, edited by Ali Sorayyaei Azar, et al., IGI Global, 2025, pp. 225-250. https://doi.org/10.4018/979-8-3373-3952-8.ch010
[8] Tony Lee, Erin Ransom, and James Morrison, The Great Resignation (in cybersecurity), Artificial Intelligence, online. https://blogs.blackberry.com/en/2021/11/the-great-resignation-in-cybersecurity
[9] Kodi D, “Multi-Cloud FinOps: AI-Driven Cost Allocation and Optimization Strategies”, International Journal of Emerging Trends in Computer Science and Information Technology, pp. 131-139, 2025.
[10] The Great Resignation, cybersecurityintelligence, 2022. Online. https://www.cybersecurityintelligence.com/blog/the-great-resignation-6682.html
[11] S. Gupta, S. Barigidad, S. Hussain, S. Dubey and S. Kanaujia, "Hybrid Machine Learning for Feature-Based Spam Detection," 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India, 2025, pp. 801-806, doi: 10.1109/CICTN64563.2025.10932459.
[12] Padmaja Pulivarthy, (2024/3/9). Semiconductor Industry Innovations: Database Management in the Era of Wafer Manufacturing. FMDB Transactions on Sustainable Intelligent Networks. 1(1). 15-26. FMDB.
[13] Proença, D., & Borbinha, J. (2018). Information security management systems-a maturity model based on ISO/IEC 27001. In Business Information Systems: 21st International Conference, BIS 2018, Berlin, Germany, July 18-20, 2018, Proceedings 21 (pp. 102-114). Springer International Publishing.
[14] Swathi Chundru, Siva Subrahmanyam Balantrapu, Praveen Kumar Maroju, Naved Alam, Pushan Kumar Dutta, Pawan Whig, (2024/12/1), AGSQTL: adaptive green space quality transfer learning for urban environmental monitoring, 8th IET Smart Cities Symposium (SCS 2024), 2024, 551-556, IET.
[15] Puneet Aggarwal. "Mastering Big Data With Sap Hana: Cutting-Edge Strategies For Scalable And Efficient Data Management In The Cloud Techniques", International Journal Of Cloud Computing (Ijcc), 1 (1), 33-52, 2023.
[16] Jony Fischbein, Insider threats: how the ‘Great Resignation’ is impacting data security, 2022, online. https://www.weforum.org/stories/2022/05/insider-threats-how-the-great-resignation-is-impacting-data-security/
[17] Venu Madhav Aragani, Arunkumar Thirunagalingam, “Leveraging Advanced Analytics for Sustainable Success: The Green Data Revolution,” in Driving Business Success Through Eco-Friendly Strategies, IGI Global, USA, pp. 229- 248, 2025.
[18] L. N. Raju Mudunuri, “Maximizing Every Square Foot: AI Creates the Perfect Warehouse Flow,” FMDB Transactions on Sustainable Computing Systems., vol. 2, no. 2, pp. 64–73, 2024.
[19] Algarni, A. M., & Malaiya, Y. K. (2016, May). A consolidated approach for estimation of data security breach costs. In 2016 2nd International Conference on Information Management (ICIM) (pp. 26-39). IEEE.
[20] Mohanarajesh Kommineni. (2022/9/30). Discover the Intersection Between AI and Robotics in Developing Autonomous Systems for Use in the Human World and Cloud Computing. International Numeric Journal of Machine Learning and Robots. 6. 1-19. Injmr.
[21] Prabhu, S., & Thompson, N. (2022). A primer on insider threats in cybersecurity. Information Security Journal: A Global Perspective, 31(5), 602-611.
[22] Kirti Vasdev. (2019). “AI and Machine Learning in GIS for Predictive Spatial Analytics”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288363
[23] Puvvada, R. K. "SAP S/4HANA Cloud: Driving Digital Transformation Across Industries." International Research Journal of Modernization in Engineering Technology and Science 7.3 (2025): 5206-5217.
[24] Robbins, S. (2022). The Federal IT Crisis: Advocating for Digital Governance and the Development of a More Robust Federal Government Cyber Workforce. Public Contract Law Journal, 52(1), 157-177.
[25] Enhanced System of Load Management for LowVoltage, Sree Lakshmi Vineetha Bitragunta, IJIRMPS2203231928, Volume 10 Issue 3 2022, PP-1-10.
[26] Jagadeesan Pugazhenthi, V., Singh, J., & Pandy, G. (2025). Revolutionizing IVR Systems with Generative AI for Smarter Customer Interactions. International Journal of Innovative Research in Computer and Communication Engineering, 13(1).
[27] Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,” Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media”, Journal of Artificial Intelligence and Big Data (jaibd),1(1),11-20,2016.
[28] Khan, S., Noor, S., Javed, T. et al. “XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites”. BioData Mining 18, 12 (2025). https://doi.org/10.1186/s13040-024-00415-8.
[29] Singhal, S., Kothuru, S. K., Sethibathini, V. S. K., & Bammidi, T. R. (2024). ERP excellence a data governance approach to safeguarding financial transactions. Int. J. Manag. Educ. Sustain. Dev, 7(7), 1-18.
