Enterprise Cognitive Security Using Agentic AI for Autonomous Threat Prediction, Policy Orchestration, and Multi-Cloud Governance
DOI:
https://doi.org/10.15662/IJEETR.2026.0803014Keywords:
Agentic Artificial Intelligence, Enterprise Cognitive Security, Autonomous Threat Prediction, Policy Orchestration, Multi-Cloud Governance, Zero Trust, Machine Learning, Cloud Security, Intelligent Automation, Cyber Risk Analytics, Continuous Compliance, Enterprise GovernanceAbstract
The rapid expansion of enterprise cloud computing has transformed organizational operations by enabling scalable, distributed, and intelligent digital infrastructures. However, the increasing complexity of hybrid and multi-cloud environments has also introduced sophisticated cybersecurity challenges, including advanced persistent threats, identity compromise, cloud misconfigurations, insider attacks, and regulatory compliance issues. Traditional security architectures relying on static policies and manual administration are no longer sufficient to address dynamic cyber risks across heterogeneous cloud ecosystems. This study proposes an Enterprise Cognitive Security architecture that leverages Agentic Artificial Intelligence to autonomously predict cyber threats, orchestrate security policies, and govern multi-cloud infrastructures through continuous learning and adaptive decision-making. The proposed framework integrates intelligent threat analytics, reinforcement learning, autonomous policy orchestration, behavioral analysis, Zero-Trust principles, and continuous governance into a unified cognitive security platform. Agentic AI continuously observes enterprise activities, predicts emerging risks, recommends mitigation strategies, and autonomously executes security controls while maintaining regulatory compliance across multiple cloud providers. The research adopts a design science methodology supported by architectural modeling, simulation-based validation, and comparative performance evaluation to assess the effectiveness of the proposed framework. Results are expected to demonstrate significant improvements in threat prediction accuracy, automated policy enforcement, operational resilience, governance efficiency, and compliance assurance. The proposed architecture offers an adaptive, scalable, and intelligent cybersecurity solution capable of supporting the evolving security requirements of modern cloud-native enterprises
References
1. Mohammed, S. (2026). Enterprise AI infrastructure and intelligent automation for future-ready organizations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 15(1), 150–164. https://doi.org/10.15662/IJAREEIE.2026.1501018
2. Gollapudi, R. (2024). Event-aware multi-layer storage risk forecasting for Oracle database estates using HAPF. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.5183
3. Baracaldo, N., Chen, B., Ludwig, H., & Safavi, J. A. (2020). Federated learning for enterprise-scale AI. IBM Journal of Research and Development, 64(2/3), 7:1–7:13.
4. Veershetty, G. (2024). Secure conversational AI: A unified enterprise architecture framework for integrating WhatsApp Business with global ERP systems. International Journal of Science, Research and Technology (IJSRAT), 7(1), 11360–11372.
5. Devineni, A. (2025). Cognitive Load Reduction in On-Call Rotations via Predictive Alert Severity Scoring Using Machine Learning in Financial Cloud Operations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 268-273.
6. Kanchumarthi, S. N. V. P. (2024, April). Hybrid network security architecture: F5–AWS integration, zero-trust enforcement, and SD-WAN for PCI DSS-compliant hybrid environments. World Journal of Advanced Research and Reviews, 22(1), 2111–2117. https://doi.org/10.30574/wjarr.2024.22.1.1162
7. Yatam, S. N. K. (2025). Autonomous DevOps: The ZTI-MDS Integration Framework. Journal of Computer Science and Technology Studies, 7(7), 755-763.
8. Lingala, B. (2025). Transforming Enterprise Transaction Data into Intelligent Decision Systems. International Journal of Computer Technology and Electronics Communication, 8(5), 11488-11494.
9. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2020). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.
10. Polamreddy, V. R. (2025). Architecting Financially Compliant Enterprise Point-of-Sale Systems: Data Integrity and Revenue Recognition at Scale. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(5), 12993-13104.
11. Hossain, I., Lindon, A. R., Rahman, M., Khan, H. A., Tohfa, N. A., Shagar, M. T. M., Shakib, J. E., & Nasif, M. R. I. (2026). Hybrid ensemble learning for robust DDoS detection and attack classification with a web-based analytical tool for cybersecurity analysts. Journal of Electrical Engineering, 11(5). https://doi.org/10.5281/zenodo.20046694
12. Beeram, S. (2026). Unified Security Posture Management across Clouds using Microsoft Defender for Cloud. International Journal of Emerging Trends in Computer Science and Information Technology, 7(2), 14-16.
13. Joyce, S. (2023). Accelerating Enterprise SAP Workload Performance and Automation Using Microsoft Azure Center for SAP Solutions Through Cloud Native Architecture Intelligent Orchestration and Infrastructure as Code. IACSE-International Journal of Information Technology (IACSE-IJIT), 4(1), 8-30.
14. Hasan, M. M., Das, A., Akash, A. H., Rahaman, M. A., Irin, K. N., & Mahi, F. F. (2026, March). Early Stage Parkinsonian Disorder Detection Using Machine Learning Classifiers and Neuro Motor Feature Analysis. In 2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) (pp. 893-899). IEEE.
15. Pothuri, M. K. (2025). Designing a Metadata-Driven Framework for Automated Data Profiling, Data Analysis, Data Management, Integration at Scale in Medicaid Healthcare Ecosystems. International Journal of Multidisciplinary Research and Growth Evaluation, 6(4), 1413-1418.
16. Gopisetty, S. (2024). When Healthcare Lags, Banking Leaks: A Generative AI Framework to Stop Time-Based Data Spills in Cross-Sector Federated Learning. International Journal of AI, BigData, Computational and Management Studies, 5(4), 238-260.
17. Singh, A. (2025). AI-driven autonomous network control planes for large-scale infrastructure networks. International Journal of Computer Technology and Electronics Communication, 8(6), 11705-11715.
18. Karnam, A. (2026). Operational Intelligence for SAP: How AI Agents Transform Incident Response and System Health. International Journal of Science, Research and Technology, 9(1), 59-67.
19. 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.
20. Sudakara, B. B. (2026). Leveraging MCP servers for context-aware playwright automation in cloud environments. Journal of Emerging Engineering Technologies, 1(1), 13-18.
21. Manda, P. (2022). Implementing hybrid cloud architectures with Oracle and AWS: Lessons from mission-critical database migrations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111–7122.
22. Veershetty, G. (2022). Digital Modernization of Gas Utility Operations: Architecture, Scaled-Agile Delivery, and Assurance. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7796.
23. Damarched, M. K. (2026). Harnessing Large Language Models and Agentic AI for Transformative Cloud Reliability and Incident Management: A Comprehensive Suggestive Review. Journal of Computer Science and Technology Studies, 8(5), 43-81.
24. Navandar, P. (2024). Governance, risk, and compliance (GRC) in the age of identity and access governance (IAG): A framework for integrated enterprise security and compliance. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10483–10493. https://doi.org/10.15662/IJRAI.2024.0702011
25. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.
26. Gurram, S. K. (2025). Revolutionizing financial infrastructure: the convergence of blockchain and cloud in next-generation payment networks. Journal of Computer Science and Technology Studies, 7(4), 607-618.
