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Conceptual Model improving Encryption Strategies for Organizational Information Protection

Ugwu-Oju Ukamaka Mary Okeke Obinna ThankGod Nwankwo Constance Obiuto

Subject area: Science,Engineering and Technology  ·  Area of research: Information Security

Abstract

The rapid expansion of digital ecosystems, intensified cyber threats, and increasingly complex regulatory mandates have amplified the need for robust, adaptable, and intelligence-driven encryption strategies within modern organizations. Traditional encryption mechanisms, while foundational, are no longer sufficient to address evolving risks such as distributed cyberattacks, insider compromise, advanced persistent threats, and emerging quantum-enabled adversaries. This abstract presents a conceptual model designed to enhance organizational information protection by integrating advanced cryptographic techniques, dynamic encryption governance, and intelligent decision-support mechanisms. The model emphasizes a multi-layered approach combining post-quantum cryptography, homomorphic encryption, and attribute-based access control (ABAC) to ensure that sensitive data remains secure across storage, transmission, and processing environments. It also incorporates continuous key rotation protocols, automated cryptographic lifecycle management, and context-aware encryption policies capable of adapting to fluctuating risk conditions. A central component of the model is its alignment with zero-trust principles, ensuring that encryption becomes closely coupled with identity verification, device trust assessment, and micro-segmentation practices. Furthermore, the conceptual model introduces an intelligence layer that leverages machine learning to predict encryption-related vulnerabilities, optimize key distribution workflows, and detect anomalous cryptographic operations indicative of compromise. It is designed to support diverse infrastructures?including cloud-native systems, hybrid environments, and edge-based architectures?while maintaining regulatory conformity with global standards such as GDPR, ISO 27001, and NIST cryptographic guidelines. Overall, the model aims to advance encryption from a static technical control to a dynamic, integrated organizational capability. By combining next-generation cryptographic technologies with adaptive governance and intelligent automation, the proposed model enhances resilience, reduces data exposure risks, and supports secure digital transformation across complex enterprise environments.

Keywords

Encryption strategies, Post-quantum cryptography, Homomorphic encryption, Organizational information protection, Zero-trust security, Cryptographic governance, Adaptive cybersecurity, Machine learning, Data security, Digital transformation.

References

[1] Abbas, N., Zhang, Y., Taherkordi, A. and Skeie, T., 2017. Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), pp.450-465.

[2] Allen, G. and Chan, T., 2017. Artificial intelligence and national security (Vol. 132). Cambridge, MA: Belfer Center for Science and International Affairs.

[3] Ani, U.P.D., He, H. and Tiwari, A., 2017. Review of cybersecurity issues in industrial critical infrastructure: manufacturing in perspective. Journal of Cyber Security Technology, 1(1), pp.32-74.

[4] Attaran, M., 2017. Cloud computing technology: leveraging the power of the internet to improve business performance. Journal of International Technology and Information Management, 26(1), pp.112-137.

[5] Bartnes, M., Moe, N.B. and Heegaard, P.E., 2016. The future of information security incident management training: A case study of electrical power companies. Computers & Security, 61, pp.32-45.

[6] Biswas, S. and Sen, J., 2017. A proposed architecture for big data driven supply chain analytics. arXiv preprint arXiv:1705.04958.

[7] Bowman, C., Gesher, A., Grant, J.K., Slate, D. and Lerner, E., 2015. The architecture of privacy: On engineering technologies that can deliver trustworthy safeguards. " O'Reilly Media, Inc.".

[8] Brasser, F., Müller, U., Dmitrienko, A., Kostiainen, K., Capkun, S. and Sadeghi, A.R., 2017. Software grand exposure:{SGX} cache attacks are practical. In 11th USENIX workshop on offensive technologies (WOOT 17).

[9] Buecker, A., Chakrabarty, B., Dymoke-Bradshaw, L., Goldkorn, C., Hugenbruch, B., Nali, M.R., Ramalingam, V., Thalouth, B. and Thielmann, J., 2016. Reduce Risk and Improve Security on IBM Mainframes: Volume 1 Architecture and Platform Security. IBM Redbooks.

[10] Carvalho Ota, F.K., Roland, M., Hölzl, M., Mayrhofer, R. and Manacero, A., 2017. Protecting touch: Authenticated app-to-server channels for mobile devices using NFC tags. Information, 8(3), p.81.

[11] Chithralekha, B., Kalpana, S., Ganeshvani, G. and Muttukrishnan, R., 2017. Post-Quantum and Code-Based Cryptography—Some Prospective Research Directions. Signature, p.44.

[12] Craig, A.N., Shackelford, S.J. and Hiller, J.S., 2015. Proactive cybersecurity: A comparative industry and regulatory analysis. American Business Law Journal, 52(4), pp.721-787.

[13] Dapp, T., Slomka, L., AG, D.B. and Hoffmann, R., 2015. Fintech reloaded–Traditional banks as digital ecosystems. Publication of the German original, pp.261-274.

[14] Escamilla-Ambrosio, P.J., Rodríguez-Mota, A., Aguirre-Anaya, E., Acosta-Bermejo, R. and Salinas-Rosales, M., 2017, September. Distributing computing in the internet of things: cloud, fog and edge computing overview. In NEO 2016: Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico (pp. 87-115). Cham: Springer International Publishing.

[15] Everspaugh, A., Paterson, K., Ristenpart, T. and Scott, S., 2017, August. Key rotation for authenticated encryption. In Annual international cryptology conference (pp. 98-129). Cham: Springer International Publishing.

[16] Fachkha, C. and Debbabi, M., 2015. Darknet as a source of cyber intelligence: Survey, taxonomy, and characterization. IEEE Communications Surveys & Tutorials, 18(2), pp.1197-1227.

[17] Fanto, J., 2016. Dashboard Compliance: Benefit, Threat, or Both. Brook. J. Corp. Fin. & Com. L., 11, p.1.

[18] Gahi, Y., Guennoun, M. and Mouftah, H.T., 2016, June. Big data analytics: Security and privacy challenges. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 952-957). IEEE.

[19] Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P. and Riviere, E., 2015. Edge-centric computing: Vision and challenges. ACM SIGCOMM Computer Communication Review, 45(5), pp.37-42.

[20] Gendreau, A.A. and Moorman, M., 2016, August. Survey of intrusion detection systems towards an end to end secure internet of things. In 2016 IEEE 4th international conference on future internet of things and cloud (FiCloud) (pp. 84-90). IEEE.

[21] Gozman, D. and Currie, W., 2015, January. Managing governance, risk, and compliance for post-crisis regulatory change: A model of IS capabilities for financial organizations. In 2015 48th Hawaii International Conference on System Sciences (pp. 4661-4670). IEEE.

[22] Hui, T.K., Sherratt, R.S. and Sánchez, D.D., 2017. Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 76, pp.358-369.

[23] Humayed, A., Lin, J., Li, F. and Luo, B., 2017. Cyber-physical systems security—A survey. IEEE Internet of Things Journal, 4(6), pp.1802-1831.

[24] Jamshidi, P., Pahl, C. and Mendonça, N.C., 2017. Pattern‐based multi‐cloud architecture migration. Software: Practice and Experience, 47(9), pp.1159-1184.

[25] Khalifa, S., Elshater, Y., Sundaravarathan, K., Bhat, A., Martin, P., Imam, F., Rope, D., Mcroberts, M. and Statchuk, C., 2016. The six pillars for building big data analytics ecosystems. ACM Computing Surveys (CSUR), 49(2), pp.1-36.

[26] Kotha, N.R., 2017. Intrusion Detection Systems (IDS): Advancements, Challenges, and Future Directions. International Scientific Journal of Contemporary Research in Engineering Science and Management, 2(1), pp.21-40.

[27] Kumar, T.V., 2016. Layered App Security Architecture for Protecting Sensitive Data.

[28] Kunz, M., Fuchs, L., Hummer, M. and Pernul, G., 2015, December. Introducing dynamic identity and access management in organizations. In International Conference on Information Systems Security (pp. 139-158). Cham: Springer International Publishing.

[29] Lei, A., Cruickshank, H., Cao, Y., Asuquo, P., Ogah, C.P.A. and Sun, Z., 2017. Blockchain-based dynamic key management for heterogeneous intelligent transportation systems. IEEE Internet of Things Journal, 4(6), pp.1832-1843.

[30] Li, X., Eckert, M., Martinez, J.F. and Rubio, G., 2015. Context aware middleware architectures: Survey and challenges. Sensors, 15(8), pp.20570-20607.

[31] Liska, A. and Gallo, T., 2016. Ransomware: Defending against digital extortion. " O'Reilly Media, Inc.".

[32] Luo, F., Zhao, J., Dong, Z.Y., Chen, Y., Xu, Y., Zhang, X. and Wong, K.P., 2015. Cloud-based information infrastructure for next-generation power grid: Conception, architecture, and applications. IEEE Transactions on Smart Grid, 7(4), pp.1896-1912.

[33] Matthews, G., Desmond, P.A., Neubauer, C. and Hancock, P.A., 2017. An overview of operator fatigue. The handbook of operator fatigue, pp.3-23.

[34] Maule, R.W., 2016, June. Complex quality of service lifecycle assessment methodology. In 2016 IEEE International Congress on Big Data (BigData Congress) (pp. 462-469). IEEE.

[35] Merfield, C.N., 2016. Robotic weeding's false dawn? Ten requirements for fully autonomous mechanical weed management. Weed Research, 56(5), pp.340-344.

[36] Mills, J.L. and Harclerode, K., 2017. Privacy, mass intrusion, and the modern data breach. Fla. L. Rev., 69, p.771.

[37] Minchev, Z., 2017. Security challenges to digital ecosystems dynamic transformation. Proc. of BISEC, pp.6-10.

[38] Mozaffari-Kermani, M., Azarderakhsh, R. and Aghaie, A., 2016. Fault detection architectures for post-quantum cryptographic stateless hash-based secure signatures benchmarked on ASIC. ACM Transactions on Embedded Computing Systems (TECS), 16(2), pp.1-19.

[39] Mylrea, M. and Gourisetti, S.N.G., 2017. Cybersecurity and optimization in smart “autonomous” buildings. In Autonomy and Artificial Intelligence: A Threat or Savior? (pp. 263-294). Cham: Springer International Publishing.

[40] O’Donovan, P., Leahy, K., Bruton, K. and O’Sullivan, D.T., 2015. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of big data, 2(1), p.25.

[41] Omopariola, M., 2017. AI-Enhanced Threat Detection for National-Scale Cloud Networks: Frameworks, Applications, and Case Studies.

[42] Pan, J. and McElhannon, J., 2017. Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal, 5(1), pp.439-449.

[43] Poller, A., Kocksch, L., Türpe, S., Epp, F.A. and Kinder-Kurlanda, K., 2017, February. Can security become a routine? A study of organizational change in an agile software development group. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing (pp. 2489-2503).

[44] Praveenkumar, P., Thenmozhi, K., Rayappan, J.B.B. and Amirtharajan, R., 2017. Inbuilt image encryption and steganography security solutions for wireless systems: a survey. Research Journal of Information Technology, 9, pp.46-63.

[45] Raj, P., Raman, A., Nagaraj, D. and Duggirala, S., 2015. High-performance big-data analytics. Computing Systems and Approaches (Springer, 2015), 1.

[46] Rassam, M.A., Maarof, M. and Zainal, A., 2017. Big Data Analytics Adoption for Cybersecurity: A Review of Current Solutions, Requirements, Challenges and Trends. Journal of Information Assurance & Security, 12(4).

[47] Schneider, G.P., Dai, J., Janvrin, D.J., Ajayi, K. and Raschke, R.L., 2015. Infer, predict, and assure: Accounting opportunities in data analytics. Accounting Horizons, 29(3), pp.719-742.

[48] Sethupathy, A. and Kumar, U., 2015. Cloud-Enabled Mobile Network Diagnostic Platforms: Real-Time Data Collection and Analytics. Journal of Emerging Technologies and Innovative Research, 2, pp.598-609.

[49] Shu, X., Yao, D. and Bertino, E., 2015. Privacy-preserving detection of sensitive data exposure. IEEE transactions on information forensics and security, 10(5), pp.1092-1103.

[50] Simmons, A.C., 2017. Tackling the barriers to achieving Information Assurance.

[51] Singh, B., 2017. Enhancing Real-Time Database Security Monitoring Capabilities Using Artificial Intelligence. International Journal of Current Engineering and Scientific Research (IJCESR).

[52] Sinha, S.R. and Park, Y., 2017. Building an E Ective IoT Ecosystem for Your Business. Springer.

[53] Sinha, S.R. and Park, Y., 2017. Building an E Ective IoT Ecosystem for Your Business. Springer.

[54] Sundaramurthy, S.C., McHugh, J., Ou, X., Wesch, M., Bardas, A.G. and Rajagopalan, S.R., 2016. Turning contradictions into innovations or: How we learned to stop whining and improve security operations. In Twelfth Symposium on Usable Privacy and Security (SOUPS 2016) (pp. 237-251).

[55] Tan, S., De, D., Song, W.Z., Yang, J. and Das, S.K., 2016. Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys & Tutorials, 19(1), pp.397-422.

[56] Vaduganathan, D., 2016. Secure data sharing using attribute based encryption with revocation in cloud computing. South Asian J. Eng. Technol., 2(15), pp.145-150.

[57] Vermesan, O. and Friess, P., 2015. Building the hyperconnected society-internet of things research and innovation value chains, ecosystems and markets (p. 332). Taylor & Francis.

[58] Ylonen, T., Turner, P., Scarfone, K. and Souppaya, M., 2015. Security of interactive and automated access management using Secure Shell (SSH). NISTIR 7966, National Institute of Standards and Technology.

[59] Zaidan, B.B., Haiqi, A., Zaidan, A.A., Abdulnabi, M., Kiah, M.M. and Muzamel, H., 2015. A security framework for nationwide health information exchange based on telehealth strategy. Journal of medical systems, 39(5), p.51.

[60] Zankl, A., Seuschek, H., Irazoqui, G. and Gulmezoglu, B., 2017. Side-channel attacks in the internet of things. Solutions for Cyber-Physical Systems Ubiquity, pp.325-357.

How to cite this paper

Ugwu-Oju Ukamaka Mary, Okeke Obinna ThankGod, Nwankwo Constance Obiuto "Conceptual Model improving Encryption Strategies for Organizational Information Protection" Iconic Research And Engineering Journals Volume 2 Issue 2 2018 Page 139-153
Ugwu-Oju Ukamaka Mary, Okeke Obinna ThankGod, Nwankwo Constance Obiuto "Conceptual Model improving Encryption Strategies for Organizational Information Protection" Iconic Research And Engineering Journals, vol. 2, no. 2, Aug. 2018
Ugwu-Oju Ukamaka Mary, Okeke Obinna ThankGod, Nwankwo Constance Obiuto (2018). Conceptual Model improving Encryption Strategies for Organizational Information Protection. Iconic Research And Engineering Journals, 2(2).
Ugwu-Oju Ukamaka Mary, Okeke Obinna ThankGod, Nwankwo Constance Obiuto "Conceptual Model improving Encryption Strategies for Organizational Information Protection" Iconic Research And Engineering Journals, vol. 2, no. 2, Aug. 2018.
@article{1712546,
      author = {Ugwu-Oju Ukamaka Mary, Okeke Obinna ThankGod, Nwankwo Constance Obiuto},
      title = {Conceptual Model improving Encryption Strategies for Organizational Information Protection},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {2},
      number = {2},
      pages = {139-153},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712546.pdf},
      abstract = {The rapid expansion of digital ecosystems, intensified cyber threats, and increasingly complex regulatory mandates have amplified the need for robust, adaptable, and intelligence-driven encryption strategies within modern organizations. Traditional encryption mechanisms, while foundational, are no longer sufficient to address evolving risks such as distributed cyberattacks, insider compromise, advanced persistent threats, and emerging quantum-enabled adversaries. This abstract presents a conceptual model designed to enhance organizational information protection by integrating advanced cryptographic techniques, dynamic encryption governance, and intelligent decision-support mechanisms. The model emphasizes a multi-layered approach combining post-quantum cryptography, homomorphic encryption, and attribute-based access control (ABAC) to ensure that sensitive data remains secure across storage, transmission, and processing environments. It also incorporates continuous key rotation protocols, automated cryptographic lifecycle management, and context-aware encryption policies capable of adapting to fluctuating risk conditions. A central component of the model is its alignment with zero-trust principles, ensuring that encryption becomes closely coupled with identity verification, device trust assessment, and micro-segmentation practices. Furthermore, the conceptual model introduces an intelligence layer that leverages machine learning to predict encryption-related vulnerabilities, optimize key distribution workflows, and detect anomalous cryptographic operations indicative of compromise. It is designed to support diverse infrastructures?including cloud-native systems, hybrid environments, and edge-based architectures?while maintaining regulatory conformity with global standards such as GDPR, ISO 27001, and NIST cryptographic guidelines. Overall, the model aims to advance encryption from a static technical control to a dynamic, integrated organizational capability. By combining next-generation cryptographic technologies with adaptive governance and intelligent automation, the proposed model enhances resilience, reduces data exposure risks, and supports secure digital transformation across complex enterprise environments.},
      keywords = {Encryption strategies, Post-quantum cryptography, Homomorphic encryption, Organizational information protection, Zero-trust security, Cryptographic governance, Adaptive cybersecurity, Machine learning, Data security, Digital transformation.},
      month = {August},
  }