Download this PDF file
Articles Open Access

AI-Powered Enterprise Architecture for Digital Resilience a Cross-Domain Approach to Security Governance Data Management and Automation

Sishub Kumar Joshi
Senior Specialist Programmer, Infosys, India

Abstract

Digital transformation has significantly increased organizational dependence on interconnected technologies, cloud computing, artificial intelligence (AI), and data-driven decision-making. While these advancements enhance operational efficiency and innovation, they also expose enterprises to evolving cybersecurity threats, governance challenges, and data management complexities. Digital resilience has emerged as a strategic capability that enables organizations to anticipate, withstand, recover from, and adapt to disruptions while maintaining business continuity. This essay explores the role of AI-powered enterprise architecture in strengthening digital resilience through an integrated cross-domain approach encompassing security governance, data management, and automation. The study examines how enterprise architecture frameworks can leverage AI technologies to improve risk detection, automate governance processes, optimize data utilization, and support adaptive decision-making. Existing literature highlights the growing importance of intelligent architectures capable of aligning business objectives with technological capabilities while addressing regulatory compliance and security requirements. The essay synthesizes theoretical and practical perspectives to develop a comprehensive understanding of AI-enabled resilience mechanisms across organizational domains. Furthermore, it investigates how automation and advanced analytics contribute to proactive threat management, operational agility, and sustainable digital transformation. The findings suggest that organizations adopting AI-driven enterprise architecture gain enhanced visibility, responsiveness, and governance effectiveness, enabling them to navigate complex digital environments more successfully. Ultimately, the integration of AI, security governance, data management, and automation provides a robust foundation for achieving long-term digital resilience and organizational competitiveness

Keywords

Artificial IntelligenceEnterprise ArchitectureDigital ResilienceSecurity GovernanceData ManagementAutomationCybersecurityDigital TransformationRisk ManagementOrganizational AgilityBusiness ContinuityIntelligent SystemsInformation GovernanceAdaptive ArchitectureEnterprise Security

DOI

https://doi.org/10.15680/IRJIE.2024.0802001

PDF Download

PDF

Publication Information

JournalInternational Research Journal of Innovative Engineering
Volume8
Number2
Pages14256-14265
Published2024-03-09
IssueVol. 8 No. 2 (2024): International Research Journal of Innovative Engineering (IRJIE)

References

1. Namdeo, A. (2024). Emotion-aware AI for customer experience process optimization. International Journal of Research and Applied Innovations (IJRAI), 7(1), 10154–10163. https://doi.org/10.15662/IJRAI.2024.0701007
2. Gollapudi, R. Backup integrity and recovery readiness assessment for high-availability databases. Computer Fraud and Security. 2024;23.
3. 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.
4. Veershetty, G. (2023). Risk-Adaptive Transition and Transformation (RATT): A Predictive Governance Framework for SAP Cloud Migration Programs.
5. Parasa, M. (2024). Intelligent compliance automation in SAP SuccessFactors: AI monitoring for global labor law adherence. International Research Journal of Engineering & Applied Sciences, 12(3). https://doi.org/10.55083/irjeas.2024.v12i03006
6. Adepu, R. (2023). Designing FedRAMP-Compliant Cloud Architectures for Secure and Scalable Government Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 10427-10441.
7. Katta, T. B. (2024). Transforming enterprise integration with cloud native innovations and next generation technology paradigms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 10347-10358.
8. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
9. Kunadi, S. K. (2022). Building scalable master data management systems for enterprise data platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(2), 4830–4843.
10. Mathew, A. (2023). The 5 Cs of cybersecurity and its integration with predictive analytics. International Journal of Computer Science and Mobile Computing, 12(1), 47-50.
11. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
12. Subramanyam, S. P. (2023). Cloud infrastructure automation and role-based access governance in Azure Kubernetes services. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8392–8400.
13. Navandar, P. (2023). Privacy preserving federated learning for distributed intrusion detection: Differential privacy guarantees, non-IID convergence, and Byzantine robustness. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9055–9062. https://doi.org/10.15662/IJRPETM.2023.0604011
14. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
15. Kotla, M. R. T. (2023). AI in consumer digital banking: Enabling smart personalization and fraud detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 262–276.
16. Boddupally, H. L. (2023). Self Improving Enterprise Platforms Using Learning Loops and AI Driven Orchestration. Available at SSRN 6270638.
17. Narayanan, S. (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
18. Vayyasi, N. K. (2023). Designing a multi-domain predictive framework using Java and generative AI for financial, retail, and industrial use cases. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8060–8069.
19. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
20. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
21. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661

Statistics

104
Views
88
Downloads