Designing Intelligent Enterprise Platforms Through Converged AI Security and Operational Analytics
Abstract
The rapid digital transformation of enterprises has increased the complexity of managing operational performance, cybersecurity threats, and data governance. Traditional enterprise platforms often treat security and operational analytics as separate functions, resulting in fragmented decision-making, delayed threat detection, and inefficient resource utilization. Converged Artificial Intelligence (AI) Security and Operational Analytics offers a unified framework that integrates cybersecurity intelligence, machine learning, predictive analytics, and business operations into a single intelligent platform. This approach enables organizations to detect anomalies, predict operational disruptions, automate responses, and optimize performance in real time. The proposed intelligent enterprise platform leverages AI-driven security monitoring, behavioral analytics, big data processing, and automated orchestration mechanisms to enhance organizational resilience. By combining security event management with operational intelligence, enterprises can improve situational awareness, reduce response times, and support strategic decision-making. The framework also facilitates proactive risk management through continuous monitoring and adaptive learning models capable of evolving with changing business and threat environments. This study examines the architectural components, implementation strategies, benefits, and challenges associated with converged AI security and operational analytics. The findings indicate that integrated platforms significantly improve operational efficiency, cybersecurity readiness, and business continuity while presenting challenges related to privacy, implementation complexity, and regulatory compliance. The research contributes to the development of next-generation intelligent enterprise ecosystems.Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
14327-14335 |
Published |
September 6, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Rafael Winterhalter (%2023). Designing Intelligent Enterprise Platforms Through Converged AI Security and Operational Analytics. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14327-14335. https://doi.org/10.15662/ijetemr.2023.0805002 |
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5. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
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7. Boddupally, H. L. (2021). Quality Forecasting and Reliability Modeling in Expansive. NET Application Landscapes. Available at SSRN 6266042.
8. Rajasekar, M., Celine Kavida, A., & Anto Bennet, M. (2020). A pattern analysis based underwater video segmentation system for target object detection. Multidimensional Systems and Signal Processing, 31(4), 1579-1602.
9. Vimal, V. R., Anandan, P., & Kumaratharan, N. (2022). Heart Disease Diagnosis Using Electrocardiography (ECG) Signals. Intelligent Automation & Soft Computing, 32(1).
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12. Katta, T. B. (2022). A Capability Maturity Framework for Event-Driven Integration: Benchmarking Kafka and Pulsar in Enterprise Environments. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9589.
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28. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.