A Holistic Framework for Unifying Data Security and Management in Modern Enterprises

Authors: Ashly Joseph

Abstract:

Modern businesses struggle significantly to secure and manage their data properly as the volume and complexity of their data both expand exponentially. Through the use of a multi-layered defense strategy, a centralized management platform, and cutting-edge technologies like AI, this research paper presents a comprehensive framework to integrate data security and management. The constraints of current data protection and management strategies, technological advancements, and the evolving threat landscape are all examined in this article. It suggests best practices for putting into practice integrated data security and governance models, placing an emphasis on ongoing adaptation. The advantages mentioned include a strengthened security posture, simpler procedures, lower costs, and reduced complexity. Additionally, issues including skill shortages, antiquated systems, and cultural obstacles are examined. Security executives and Chief Information Security Officers are given practical advice on how to evaluate, plan, and put into place strong data-centric security and management capabilities. The goal of the paper is to provide a thorough study of the data security and management landscape and to arm contemporary businesses with the knowledge they need to be proactive in protecting their data assets.

Keywords: Data security, security management, cloud computing, cybersecurity, data governance, security architecture, data management.

References:

[1] Sridharan, C. (2018). Distributed Systems Observability: A Guide to Building Robust Systems. O'Reilly Media.
[2] Joseph, A. (2023). Demystifying Full-Stack Observability: Mastering Visibility, Insight, and Action in the Modern Digital Landscape. International Journal of Computer and Information Engineering, 17(8), 485-492.
[3] Sajeev, S. (2023). 'An Overview of Project Management Application in Computational Fluid Dynamics'. World Academy of Science, Engineering and Technology, Open Science Index 195, International Journal of Industrial and Manufacturing Engineering, 17(3), 202 - 208
[4] A. Randazzo and I. Tinnirello, “Kata Containers: An Emerging Architecture for Enabling MEC Services in Fast and Secure Way,” 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Granada, Spain, 2019.
[5] Sajeev, S. K. (2016). Sand Erosion of Gas-Liquid Cylindrical Cyclone Separators Under Gas Production and Low-Liquid Loading Conditions (Doctoral dissertation, University of Tulsa).
[6] Newman, S. (2015). Building Microservices: Designing Fine-Grained Systems. O'Reilly Media.
[7] G. Rezende Alles, A. Carissimi and L. Mello Schnorr, “Assessing the Computation and Communication Overhead of Linux Containers for HPC Applications,” 2018 Symposium on High Performance Computing Systems (WSCAD), São Paulo, Brazil, 2018, pp. 116-123.
[8] Rabl, T., & Gómez-Villamor, S. (2014). Nephele/PACTs: a programming model and execution framework for web-scale analytical processing. In Proceedings of the 1st ACM symposium on Cloud computing (SoCC '10). Association for Computing Machinery, New York, NY, USA, 119–130. DOI: https://doi.org/10.1145/1807128.1807141
[9] Sajeev, S. K. (2019). Particle Transport in Horizontal Pipes for Single-Phase and Multiphase Flows at Very Low Concentrations Including the Threshold Concentration. The University of Tulsa.
[10] A. Randazzo and I. Tinnirello, “Kata Containers: An Emerging Architecture for Enabling MEC Services in Fast and Secure Way,” 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Granada, Spain, 2019.
[11] Vieira, R. E., Sajeev, S., Shirazi, S. A., McLaury, B. S., & Kouba, G. (2015, June). Experiments and modelling of sand erosion in gas-liquid cylindrical cyclone separators under gas production and low-liquid loading conditions. In 17th International Conference on Multiphase Production Technology. OnePetro.
[12] A. B. S., H. M.J., J. P. Martin, S. Cherian and Y. Sastri, “System Performance Evaluation of Para Virtualization, Container Virtualization, and Full Virtualization Using Xen, OpenVZ, and XenServer,” 2014 Fourth International Conference on Advances in Computing and Communications, Cochin, 2014, pp. 247-250.
[13] Kareepadath Sajeev, S. (2020). Application of Deep Learning for Understanding Dynamic Well Connectivity (Doctoral dissertation).
[14] Zhou, X., Abel, D., Truffet, D., 1998. Data partitioning for parallel spatial join processing, in: Geoinformatica, Springer-Verlag. pp. 175-204.
[15] Sajeev, S., McLaury, B., & Shirazi, S. (2017). Critical Velocities for Particle Transport from Experiments and CFD Simulations. International Journal of Environmental and Ecological Engineering, 11(6), 548-552.
[16] Zhong, Y., Han, J., Zhang, T., Li, Z., Fang, J., Chen, G., 2012. Towards parallel spatial query processing for big spatial data, in: Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 2085-2094.
[17] Sajeev, S., McLaury, B. S., & Shirazi, S. A (2018, June). Threshold Particle Concentration in Single-Phase and Multiphase Flow Sand Transport in Pipeline. 11th North American Conference on Multiphase Production Technology. OnePetro.
[18] Kwon, O., Li, K.J., 2011. Progressive spatial join for polygon data stream, in: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM.
[19] Parsi, M., Vieira, R., Sajeev, S. K., McLaury, B. S., and S. A. Shirazi. "Experimental Study of Erosion in Vertical Slug/Churn Flow." Paper presented at the CORROSION 2015, Dallas, Texas, March 2015.
[20] Kwon, O., Li, K.J., 2011. Progressive spatial join for polygon data stream, in: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM.
[21] Pahl, C., & Jamshidi, P. (2016). Microservices: a systematic mapping study. In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) (pp. 137-146).
[22] Sajeev, Sajith K., Brenton S. McLaury, and Siamack A. Shirazi. "Experiments and Modelling of Critical Transport Velocity of Threshold (Very Low) Particle Concentration in Single-Phase and Multiphase Flows." BHR 19th International Conference on Multiphase Production Technology. OnePetro, 2019.
[23] A. M. Joy, “Performance comparison between Linux containers and virtual machines,” 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, 2015, pp. 342-346.
[24] Abel, D., Ooi, B., Tan, K.L., Power, R., Yu, J., 1995. Spatial join strategies in distributed spatial dbms, in: Proceedings of the 4th International Symposium on Advances in Spatial Databases
[25] Arabnejad, H., S. Sajeev, A. Guimmarra, R. Vieira, and S. A. Shirazi. "Experimental Study and Modeling of Sand Erosion in the Gas-Liquid Cylindrical Cyclone GLCC Separators." In SPE Annual Technical Conference and Exhibition. OnePetro, 2016.
[26] Bruno, R., & Rodrigues, H. (2019). Cloud-native applications: A case study to identify research topics. IEEE Access, 7, 143625-143635. DOI: 10.1109/ACCESS.2019.2945488.
[27] Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J., 1998. Scalable sweeping-based spatial join, in: Proceedings of the 24th International Conference on Very Large Databases, pp. 570-581.
[28] Huang, Y.W., Jing, N., Rundensteiner, E., 1997. Integrated query processing strategies for spatial path queries, in: Proceedings of the 13th International Conference on Data Engineering, pp. 477-486. doi:10.1109/ICDE.1997.582010.
[29] Kareepadath Sajeev, S. (2020). Application of Deep Learning for Understanding Dynamic Well Connectivity (Doctoral dissertation).