Cognitive Cyber Intelligence for Enterprise Cloud Security Through AI-Based Knowledge Graphs and Threat Correlation
DOI:
https://doi.org/10.15662/IJEETR.2026.0806001Keywords:
Cognitive Cyber Intelligence, Enterprise Cloud Security, AI-Based Knowledge Graphs, Threat Correlation, Artificial Intelligence, Graph Neural Networks, Cyber Threat Intelligence, Security Analytics, Anomaly Detection, Intelligent Threat Detection, Cloud Infrastructure SecurityAbstract
The increasing complexity of enterprise cloud infrastructure has created significant cybersecurity challenges involving distributed applications, interconnected services, dynamic workloads, and sophisticated cyberattacks. Traditional security monitoring systems often analyze security events independently, limiting their ability to identify relationships between threats, compromised resources, and coordinated attack activities. This research proposes a cognitive cyber intelligence framework for enterprise cloud security through artificial intelligence-based knowledge graphs and intelligent threat correlation. The proposed framework integrates machine learning, knowledge graph construction, natural language processing, behavioral analytics, and cyber threat intelligence to improve security monitoring and incident investigation. Security entities, including users, IP addresses, cloud resources, applications, vulnerabilities, malware indicators, and attack techniques, are represented as interconnected knowledge graph components. Graph-based reasoning mechanisms identify suspicious relationships, correlate distributed security events, and recognize potential attack sequences across enterprise cloud environments. AI-driven analytical models evaluate behavioral deviations, identify malicious communication patterns, and prioritize cybersecurity incidents according to their potential impact. Furthermore, cognitive intelligence mechanisms support contextual threat interpretation, explainable risk assessment, and automated security recommendations. The framework enables security administrators to investigate complex attack relationships and improve incident response coordination through unified threat intelligence. The proposed approach aims to strengthen threat visibility, improve detection effectiveness, reduce alert fragmentation, and enhance the resilience of distributed enterprise cloud infrastructure through intelligent knowledge-driven security management.
References
1. Wu, C., Horiuchi, S., Murase, K., Kikushima, H., & Tayama, K. (2021). Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system. Journal of Cloud Computing, 10, Article 30. https://doi.org/10.1186/s13677-021-00242-w
2. Choubey, S., & Karmakar, G. P. (2021). Artificial intelligence techniques and their application in oil and gas industry. Artificial Intelligence Review, 54, 3665–3683. https://doi.org/10.1007/s10462-020-09935-1
3. Neela, S. (2025). Transforming society through real-time data processing and enterprise integration: A societal, ethical, and economic paradigm. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13489–13495.
4. Sahu, S. K. (2025). Cloud-Native Blueprint for Healthcare Payer CRM and Care Management on Salesforce Industries (Vlocity). Journal of Computer Science and Technology Studies, 7(6), 1135–1139.
5. Mesala, A. R., Nelavala, M. M., Mohan, S., Gautam, A., Kumar, R., & Bayya, A. K. (2026). Agentic AI & Large Language Models for Enterprise Decision Intelligence and Business Process Automation. International Journal of Artificial Intelligence and Machine Learning, 6(11s), 921–928.
6. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
7. Mirani, Ajay. (2026). Architecture Patterns For Large-Scale Financial Reconciliation And Transaction Intelligence Systems. International Journal of Artificial Intelligence and Machine Learning, 6(8s), 97–111.
8. Vemireddy, S. (2023). Engineering regulatory-aware data processing systems for tax advisory platforms. International Journal of Science, Research and Technology, 6(1), 9243–9249.
9. Randhi, K. S. (2021). Design and implementation of an API-driven hybrid integration framework for real-time utility inventory systems. International Journal of Emerging Trends in Engineering and Management Research, 6(2), 9281–9294.
10. Gurram, S. (2025). Training Data Provenance and IP Compliance at Enterprise Scale. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 405–416.
11. Singh, V. (2026). Real-Time Financial Data Validation Platform for Enterprise Accounting Systems. International Journal of Computer Information Systems & Industrial Management Applications, 18, 467.
12. Natta, P. K. (2025). AI-driven decision intelligence: Optimizing enterprise strategy with AI-augmented insights. Journal of Computer Science and Technology Studies, 7(2), 146–152.
13. Narra, S. L. (2026). Cyberbullying and psychological trauma: Digital harassment, mental health consequences, and resilience in the connected age. International Multidisciplinary Book Series (ISSN: 3139-5708), 4(1 (s)), 217–234.
14. Madabathula, L. (2025). Automated data quality enforcement using AI-assisted metadata validation in cloud-based enterprise platforms. International Journal of Applied Mathematics, 38(11s), 3514–3536.
15. Pal, A. (2025). From months to weeks: How embedded-finance platforms are re-engineering the way brands become banks. International Journal of Advances in Signal and Image Sciences, 11(6s), 2096–2104.
16. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7427–7436.
17. Nekkalapudi, K. (2026, July). Full-Stack Backend Development with Java, MongoDB, and AWS. In 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS) (pp. 1321–1326). IEEE.
18. Kannan, K. S., Sunitha, G., Deepa, S. N., Babu, D. V., & Avanija, J. (2022). A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Computers and Electrical Engineering, 102, 108225.
19. Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1–7). IEEE.
20. Varanasi, B. S. S. (2025). AI-Enabled Continuous Authority-to-Operate (ATO) Frameworks for Information Systems. Enterprise Development and Microfinance, 35(2), 748–757.
21. Kommineni, R. (2026). Guarded autonomy for alert-driven remediation: Context-aware decisions and crash-safe execution in multi-cloud Kubernetes. International Journal of Engineering & Extended Technologies Research, 8(1), 262–268.
22. Chinthamani, R. (2025). Reliable Credential Rotation and Runtime Secret Convergence in Large-Scale Cloud-Native Systems. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14617.
23. Ramakrishnan, S., Rajbongshi, B. J., & Obuladinne, S. C. (2026). Mitigating the Autonomy–Friction Paradox: A Standardized Trust-Aware Framework for Agent-to-Agent Stablecoin Payments in Agentic Commerce. International Journal of Research & Technology, 14(3), 961–983.
24. 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.
25. Meghani, Viren and korepu, Manoj and Gudise, VenkateswaraReddy. (2026, August 25). SLM-Augmented Regulatory Impact Propagation: A Lightweight Architecture for Compliance-aware Change Analysis in Legacy Banking Systems. Available at SSRN: https://ssrn.com/abstract=7545478 or http://dx.doi.org/10.2139/ssrn.7545478
26. Reddy, K. V. (2024). Optimizing gate-level simulation performance through cloud-based distributed computing. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11747–11752.
27. Rashy, R. A. (2026). Empirical Validation of Predictive Project Controls for Early Schedule-Risk Detection: Analysis of CPM Float Erosion and Milestone Performance in Construction Projects. International Journal of Artificial Intelligence and Computer Electronics, 2(4), 17–24.
28. 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.
29. Sayyaparaju, K. K. (2026, June). Autonomous Data Pipelines: Leveraging LLM Agents for Self-Healing and Adaptive ETL Systems. In 2026 5th International Conference on Computer Networks, Big Data and IoT (ICCBI) (pp. 720–726). IEEE.
30. Kalakoti, M. K. R. (2024). SmartScale: Real-time AI-driven resource optimization for cloud microservices. International Journal of Engineering & Extended Technologies Research, 6(4), 8189–8197.
31. Chayka, D. P. (2026). The Role of Packaging Automation in Improving Operational Efficiency, Productivity, and Manufacturing Performance in Corrugated Packaging Operations. International Journal of Innovation in Electronics and Computer Information Technology, 3(4), 1–8.
32. Manoharan, D. (2026). Advancing healthcare EDI interoperability through Informatica Cloud B2B Gateway quality engineering. International Journal of Computational and Experimental Science and Engineering, 12(1), 847–857.
33. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
34. Panda, M. R. (2026). Intelligent personalization in digital banking using quantum computing and real-time analytics. International Research Journal of Innovative Engineering, 10(4), 19377–19388.
35. Katakam, R., & Nalwade, A. P. (2026). Machine Learning as a Service (MLaaS): Enabling Scalable AI. Next‐Generation Technologies in Cloud Computing: From AI and Security to Sustainability, 75–89.
36. Shaik, N. (2024). Right-sizing at scale: A data-driven approach to Kubernetes cost optimization. International Journal of Future Innovative Science and Technology, 7(1), 12068–12078.
37. Gadige, C. D. (2023). API-led connectivity in enterprise CRM: A reference architecture for Salesforce and heterogeneous enterprise systems. International Research Journal of Innovative Engineering, 7(2), 12125–12135.
38. Aaviti, P. K. (2026). AI-Ready Data Lakehouse Architecture for Banking and Financial Services. International Journal of Research and Applied Innovations, 9(3), 691–701.
39. 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.
40. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131–8142.
