Cognitive Computing for Digital Transformation and Intelligent Enterprise Security Operations Management Frameworks
DOI:
https://doi.org/10.21590/Keywords:
Cognitive Computing, Digital Transformation, Intelligent Enterprise, Security Operations Management, Artificial Intelligence, Machine Learning, Cybersecurity, Threat Intelligence, Anomaly Detection, Automated ResponseAbstract
Cognitive computing has emerged as an important technological paradigm for supporting digital transformation and strengthening enterprise security operations management. By integrating artificial intelligence, machine learning, natural language processing, knowledge representation, behavioral analytics, and automated reasoning, cognitive computing enables organizations to interpret complex operational data and support intelligent security decision-making. This research proposes a cognitive computing framework for intelligent enterprise security operations that integrates heterogeneous enterprise data, continuous monitoring, contextual threat analysis, predictive intelligence, automated incident prioritization, and adaptive response mechanisms. The proposed framework is designed to improve security visibility across cloud, hybrid, and distributed enterprise environments while reducing the limitations of conventional rule-based security operations. The methodology combines literature-driven framework development, enterprise security data integration, cognitive analytics, machine learning-based anomaly detection, knowledge-driven reasoning, risk assessment, and performance evaluation. The framework considers security events, user behavior, application activity, network telemetry, infrastructure conditions, and threat intelligence as interconnected sources of operational knowledge. Cognitive decision mechanisms are subsequently used to identify anomalous patterns, correlate security events, estimate risk, and recommend appropriate responses. The expected outcomes include improved threat detection, reduced response time, enhanced operational visibility, greater automation, and improved adaptability to evolving cyber threats. The study demonstrates how cognitive computing can provide an intelligent foundation for secure and resilient digital enterprise transformation.
References
1. Kerman, A., & Cukier, M. (2021). Survivable zero trust for cloud computing environments. Computers & Security, 110, 102419. https://doi.org/10.1016/j.cose.2021.102419
2. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
3. Jain, R. (2018). Beyond stateless: A production architecture for running distributed databases on Kubernetes at scale. International Journal of Emerging Trends in Engineering and Management Research, 3(5), 4165–4172.
4. Gowda, M. K. S. (2024). Leveraging machine learning to enhance accuracy and efficiency in regulatory compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683–10692.
5. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
6. Khare, A., Khare, S., Goel, O., & Goel, P. (2024). Strategies for successful organizational change management in large digital transformation. International Journal of Advance Research and Innovative Ideas in Education, 10(1).
7. Mathew, A. (2021). Artificial intelligence for offence and defense-the future of cybersecurity. Educational Research, 3(3), 159-163.
8. Selvarajan, K. (2023). Designing multi-cloud data platforms for large-scale enterprise workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5992–6002.
9. Badam, L. R. (2023). Explainable AI framework for real-time financial fraud detection in banking systems. International Journal of Research and Applied Innovations, 6(2), 8651–8661.
10. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
11. Vemireddy, S. (2023). Building resilient enterprise platforms using event-driven and distributed computing models. International Journal of Research and Applied Innovations, 6(6), 10106-10112.
12. Badam, L. R. (2024). AI-based early warning system for financial scams targeting consumers. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10593–10602.
13. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
14. Reddy, K. V. (2024). Accelerating functional coverage closure through iterative machine learning. International Journal of Computer Applications & Information Technology, 7, 1401-1411.
15. Manda, P. (2024). Oracle E-Business Suite modernization: The transition from 12.1.3 to 12.2.8. International Journal of Research and Applied Innovations, 7(3), 10838–10842.
16. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
17. Ambati, K. C. (2024). Enterprise-wide procurement consolidation: Ivalua-SAP-EDW integration architecture for global supply chain excellence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 14309–14318.
18. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143–156.
19. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
20. Rahul Rao Juvvadi. (2024). Large language models in FP&A: An architecture for grounded variance commentary and driver-based narrative generation. Enterprise Development and Microfinance, 34(1), 71–84.
21. Bellundagi, M. (2023). Design of an intelligent clinical decision support system using machine learning techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
22. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
23. Vedula, J. (2024). A security-integrated agile governance model for IT and operational technology modernisation in the oil and gas sector. International Journal of Research and Applied Innovations, 7(4), 11191–11196.
24. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
25. Padmanabham, S. (2023). Secure API gateway architecture for open banking. International Journal of Computer Technology and Electronics Communication, 6(6), 8166–8174.
26. Sandhu, Y. S. (2024). Real-time ETL optimization using change data capture incremental. International Journal of Engineering & Extended Technologies Research, 6(3), 8184–8193.
27. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep learning-based dynamic user alignment in social networks. ACM Journal of Data and Information Quality, 15(3), 1–26.
28. Bandhela, R. R., Onteddu, A. R., & Kundavaram, R. R. (2022). Enhancing precision healthcare machine learning for advanced diagnostics and personalized treatment. South Eastern European Journal of Public Health. https://doi.org/10.70135/seejph.vi.6690
29. Sivakumer, D. (2023). Depth of ServiceNow platform utilization and business delivery performance in enterprise project management. International Journal of Computer Technology and Electronics Communication, 6(6), 8167–8177.
30. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
31. Natta, P. K. (2022). Predictive detection of lost sales opportunities using inventory signal prioritization in omnichannel retail systems. International Journal of Computer Technology and Electronics Communication, 5(4), 5483–5495.
32. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of Medical Systems, 43(4), 96.
33. Panker, T., & Nissim, N. (2021). Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments. Knowledge-Based Systems, 226, 107095. https://doi.org/10.1016/j.knosys.2021.107095
34. Katakam, R. (2023). Integrating SAP finance and banking systems for efficient enterprise financial management. International Journal of Research and Applied Innovations, 6(2), 8639–8650.
35. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Journal of Engineering & Extended Technologies Research, 4(6), 5738–5794.
36. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Computer Technology and Electronics Communication, 6(6), 8207–8217.
