Context-Aware Security Intelligence Using Multimodal AI for Digital Enterprise Resilience and Threat Analysis

Seun Michael Oyekunle; Seun Michael Oyekunle

doi:10.15662/ijetemr.2026.1105004

Context-Aware Security Intelligence Using Multimodal AI for Digital Enterprise Resilience and Threat Analysis

Seun Michael Oyekunle

Independent Researcher, USA

Abstract

The increasing complexity of digital enterprise environments has created new cybersecurity challenges involving heterogeneous data sources, distributed infrastructures, sophisticated attacks, and rapidly changing operational conditions. Conventional security mechanisms frequently analyze isolated data streams, limiting their ability to understand relationships among user behavior, network activity, application events, endpoint telemetry, and business context. This research proposes a context-aware security intelligence framework using multimodal artificial intelligence (AI) to strengthen digital enterprise resilience and threat analysis. The proposed approach integrates multiple security modalities, including structured logs, network traffic, endpoint telemetry, identity events, application data, textual threat intelligence, and operational metadata. Multimodal AI models process these heterogeneous inputs to identify cross-modal relationships, detect anomalous activities, classify threats, generate contextual risk assessments, and support adaptive response decisions. The framework incorporates context extraction, multimodal feature representation, attention-based fusion, threat correlation, risk scoring, explainable decision support, and automated resilience mechanisms. A research methodology based on simulated enterprise security data, multimodal preprocessing, AI model development, controlled attack scenarios, and comparative evaluation is established. Performance is evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, response time, and resilience indicators. The proposed framework aims to improve situational awareness by transforming fragmented security telemetry into contextual intelligence capable of supporting proactive threat analysis and resilient digital enterprise operations.

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2026.1105004
Pages	21998-22006
Published	September 10, 2026
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Seun Michael Oyekunle (%2026). Context-Aware Security Intelligence Using Multimodal AI for Digital Enterprise Resilience and Threat Analysis. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21998-22006. https://doi.org/10.15662/ijetemr.2026.1105004

References

1. Hu, K., Gong, S., Zhang, Q., Seng, C., Xia, M., et al. (2024). An overview of implementing security and privacy in federated learning. Artificial Intelligence Review, 57, 204. https://doi.org/10.1007/s10462-024-10846-8
2. Agarwal, S. (2025). Observability in stateful workloads: Strategies for monitoring persistent services in dynamic cloud environments. International Journal of Computer Technology and Electronics Communication, 8(5), 11631–11636.
3. 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.
4. Vineetha, B., Surendran, R., & Madhusundar, N. (2024, November). Enhancing accuracy in obesity prediction and nutrition guidance through KNN and decision tree models. In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI) (pp. 757-762). IEEE.
5. Goyal, K. K., Hebbar, K. S., & Mali, R. K. (2026, March). AI Modernization Enabled by Cloud-Native and Edge-Intelligent Architectures. In International Conference on Information Technology and Artificial Intelligence (pp. 102-114). Cham: Springer Nature Switzerland.
6. Badam, L. R. (2023). Explainable AI framework for real-time financial fraud detection in banking systems. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13241–13251.
7. Chaturvedi, V., Narra, R., & Chintagunta, S. K. (2026). Applied AI engineering for developers: Building intelligent applications at scale. Wissira Press. https://doi.org/10.63345/WP-978-93-7559-963-0
8. 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.
9. Manda, P. (2026). Database modernization - Sybase to SQL Server migration. International Journal of Engineering & Extended Technologies Research, 8(1), 252–258.
10. Nisar, K. (2025). Quantifying the cost and risk of enterprise LLM adaptation strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(3), 12162-12169.
11. 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.
12. Mudunuri, L. N. R., Aragani, V. M., & Maroju, P. K. (2024, December). Development of an AI-Powered Garbage Detection System for Environmental Sustainability Via YOLOv5. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 317-327). Singapore: Springer Nature Singapore.
13. Padmanabham, S. (2025). AI-Augmented Business Process Automation: Architecture and Implementation in Regulated Industries. Journal Of Multidisciplinary, 5(7), 983-991.
14. 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.
15. Mathew, A. (2021). Artificial intelligence for offence and defense-the future of cybersecurity. Educational Research, 3(3), 159-163.
16. Caso, N., Patel, K., & Sun, T. (2026). Passive acoustic dynamic differentiation and mapping (PADAM): A time-domain passive cavitation localization and classification approach. IEEE Transactions on Biomedical Engineering, 73(2), 953–963.
https://doi.org/10.1109/TBME.2025.3596596
17. 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).
18. Sureshkumar, A., Maragatharajan, M., Jangiti, K., Karuppasamy, M., Jayabalan, K., Raut, P. T., & Sivakumar, N. R. (2026). A lattice-integrated AES framework for ultra-secure biometric protection on resource-constrained edge devices. Scientific Reports, 16(1), 7254.
19. Anand, L. (2023). Leveraging Artificial Intelligence for Enterprise Modernization with Intelligent Automation and Cloud Native Computing. International Journal of Future Innovative Science and Technology (IJFIST), 6(5), 11375.
20. Ahuja, D. (2026, April). Declarative Multi-Cluster Kubernetes Deployment Architecture Using GitOps. In 2026 International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) (pp. 1-6). IEEE.
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. Tarakampet, S., Tatavarthi, S., & Ali, S. B. S. (2026, May). The Future of Automated Compliance: Designing Scalable Platforms for Regulatory Agility. In 2026 IEEE World AI IoT Congress (AIIoT) (pp. 0974-0980). IEEE.
23. 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.
24. Polamarasetty, V. K. (2025). Scalable Workday benefits integration frameworks for enterprise HR technology. International Journal of Computer Technology and Electronics Communication, 8(2), 10483–10489.
25. 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.
26. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
27. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
28. 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.
29. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
30. Mohile, A., Kumar, P., Davis, J., & Mohammed, H. S. (2026, May). An Intelligent Hybrid Framework for Network Intrusion Detection Using Deep and Machine Learning. In 2026 2nd International Conference on Computing, Communication and Green Engineering (CCGE) (pp. 1-6). IEEE.
31. Bandaru, P. K. (2025). Achieving production readiness in software-defined vehicle platforms through comprehensive verification. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11789-11793.
32. 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.
33. 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.
34. Pothuri, M. K. (2025). Building Self-Service BI in the Cloud with AI Integration: Power BI and Snowflake. International Journal of Emerging Trends in Computer Science and Information Technology, 256-262.
35. Kalakoti, M. K. R., & Kalakoti, M. K. R. (2025). AI-augmented self-healing infrastructure: Combining health probes with remediation playbooks. Journal of Information Systems Engineering and Management, 10(58s), 1147-1157.
36. 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.
37. Sandhu, Y. S. (2024). Real time ETL optimization using change data capture incremental. International Journal of Emerging Trends in Engineering and Management Research, 9(3), 15711–15721.
38. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
39. 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.