A Unified Architecture for AI-Driven Enterprise Resilience Risk Mitigation and Digital Modernization
Abstract
The rapid evolution of digital technologies has transformed the operational landscape of modern enterprises, creating new opportunities while simultaneously introducing complex risks. Organizations face increasing challenges related to cybersecurity threats, operational disruptions, regulatory compliance, and technological obsolescence. Artificial Intelligence (AI) has emerged as a critical enabler for enhancing enterprise resilience, mitigating risks, and accelerating digital modernization initiatives. This study proposes a unified architecture that integrates AI-driven analytics, risk management frameworks, cybersecurity intelligence, cloud infrastructure, and business continuity mechanisms into a comprehensive enterprise ecosystem. The proposed architecture leverages machine learning, predictive analytics, automation, and real-time monitoring to identify vulnerabilities, assess potential threats, and optimize decision-making processes. Furthermore, the architecture facilitates seamless digital transformation by integrating legacy systems with modern cloud-native platforms and intelligent automation tools. The framework enhances organizational agility, operational efficiency, and strategic adaptability while reducing exposure to financial, operational, and technological risks. By combining resilience engineering principles with AI-enabled governance and modernization strategies, the proposed model provides a scalable and sustainable solution for enterprises operating in dynamic and uncertain environments. The study contributes to existing research by offering a holistic architectural approach that aligns business objectives, technological innovation, and risk management practices to support long-term organizational sustainability and competitive advantage.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 3 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
9926-9933 |
Published |
June 13, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sannareddy Hemalatha (%2023). A Unified Architecture for AI-Driven Enterprise Resilience Risk Mitigation and Digital Modernization. International Journal of Science, Research and Technology , Vol. 6 No. 3 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 9926-9933. https://doi.org/10.15662/IJSRAT.2023.0603002 |
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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).
10. Adepu, G. (2021). Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud-Native Service Architecture. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3089-3093.
11. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
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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18. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
19. Panyala, V. R. (2022). Pioneering Kubernetes-based microservices architectures for high-throughput digital services. International Journal of Computer Technology and Electronics Communication, 5(2), 1–13.
20. Prasad, G. L. V., Nalini, T., & Sugumar, R. (2018). Mobility aware MAC protocol for providing energy efficiency and stability in mobile WSN. International Journal of Networking and Virtual Organisations, 18(3), 183-195.
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