Cognitive AI Cyber Defense for Enterprise Cloud Security Through Knowledge Graphs and Automated Incident Correlation
Keywords:
Cognitive Artificial Intelligence, Enterprise Cloud Security, Knowledge Graphs, Automated Incident Correlation, Cyber Threat Intelligence, Anomaly Detection, Security Orchestration.Abstract
The growing complexity of enterprise cloud environments has increased the need for intelligent cybersecurity mechanisms capable of identifying, correlating, and responding to sophisticated cyber threats. Traditional security systems frequently analyze alerts independently, resulting in fragmented threat visibility, excessive false positives, delayed investigations, and inefficient incident response. This research proposes a cognitive artificial intelligence (AI) cyber defense framework that integrates knowledge graphs, machine learning, contextual reasoning, and automated incident correlation to strengthen enterprise cloud security. The proposed framework collects security telemetry from cloud infrastructure, network services, identity management platforms, applications, and endpoint protection systems. These heterogeneous events are normalized and transformed into interconnected knowledge graph entities representing users, devices, workloads, vulnerabilities, network connections, and security incidents. Graph-based reasoning identifies relationships among suspicious events, while AI-driven analytics detect behavioral anomalies, infer potential attack paths, and prioritize correlated incidents according to their estimated severity. Automated orchestration subsequently recommends or executes appropriate mitigation actions, including access restriction, suspicious session termination, workload isolation, and escalation to security analysts. The methodology evaluates detection precision, recall, false-positive rate, incident correlation accuracy, response latency, and computational overhead against conventional rule-based and machine-learning baselines. The proposed approach aims to improve contextual threat understanding, reduce alert fatigue, and accelerate incident investigation across distributed enterprise cloud systems. By combining structured knowledge representation with cognitive analytics and controlled automation, this framework provides a foundation for adaptive, explainable, and scalable cyber defense while maintaining operational reliability, privacy, and human oversight.
References
Senyo, P. K., Addae, E., & Boateng, R. (2018). Cloud computing research: A review of research themes, frameworks, methods and future research directions. International Journal of Information Management, 38(1), 128–139. https://doi.org/10.1016/j.ijinfomgt.2017.07.007
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). Above the clouds: A Berkeley view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672
Pradhan, C. (2025). The Intersection of Blockchain Technology and AI in Finance. In The Impact of Artificial Intelligence on Finance: Transforming Financial Technologies (pp. 323-341). Cham: Springer Nature Switzerland.
Shaik, N. (2024). AI-driven scheduling for multi-cloud Kubernetes: Lessons from enterprise deployment. International Journal of Research and Applied Innovations, 7(3), 10880–10891.
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.
Manoharan, D. (2026). Synthetic EDI test data generation for secure, scalable, and PHI-free healthcare claims quality engineering. Journal of International Crisis and Risk Communication Research, 9(1), 358–371.
Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
Sayyaparaju, K. K. (2025, November). Q-Trans: Quantization-Aware Fine-Tuning of Transformer Models for Energy-Efficient IoT and Edge Deployment. In 2025 International Conference on Intelligent Computing, Information and Control Systems (ICOIICS) (pp. 494-499). IEEE.
Kollu, R. K. (2026). Securing the cloud: Role-based access and secrets management. International Journal of Computer Technology and Electronics Communication, 9(2), 630–636.
Ahuja, D. (2025, August). Intelligent Failure Prediction in CI/CD Pipelines Using Efficient Machine Learning Techniques. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-7). IEEE.
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.
Reddy, K. V. (2025). Automating UVM frameworks using artificial intelligence and machine learning for complex SoC verification. International Journal of Research and Applied Innovations, 8(6), 13283-13287.
Madabathula, L. (2026, May). A Resilient Multi-Petabyte Lakehouse Architecture for Regulated Enterprises: Design Patterns and Failure Recovery. In 2026 2nd International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) (pp. 731-735). IEEE.
Katakam, R., Kosna, S. R., & Rudra, A. (2025). AI-augmented predictive quality control in additive manufacturing supply chains. Journal of Business and Management Studies, 7(7), 01-08.
Karuturi, V. (2026). Enterprise observability platform for SAP systems using distributed tracing and log intelligence. International Journal of Research Publications in Engineering, Technology and Management, 9(1), pp. 258–267.
Tokas, D. (2026). Distributed big data frameworks and high-scale service design: Proven engineering patterns across cloud-native deployments. International Journal of Computational and Experimental Science and Engineering, 12(1).
Manigandan, A., Bandari, U., Thilagaraj, S., Kondisetty, K., Anbalagan, A., & Soundarapandiyan, R. (2026, June). Deep Learning Based Multi-Object Detection for Smart Surveillance Applications. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE.
Vemireddy, S. (2022). Architecting personalized tax advisory experiences through event-driven customer data integration. International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7923–7929.
Addepalli, D. N. V. (2026). An Autonomous Agentic Ai Framework For Coverage-Guided Chaos Engineering And Resilience Optimisation In Cloud-Native Distributed Systems. International Journal of Artificial Intelligence and Computer Electronics, 2(1), 80-89.
Garg, D. (2025). Warehouse management system with IoT: A comprehensive guide. International Journal of Computer Engineering & Technology, 16, 2320–2332.
Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.
Selvarajan, K. (2023). Improving data reliability through automated data quality frameworks in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10630–10640.
Sahu, S. K. (2025). Privacy-Preserving AI Architectures for Salesforce Healthcare CRM: A Human-Centered Framework for Trustworthy Intelligence in Regulated Care Operations. Frontiers in Computer Science and Artificial Intelligence, 4(5), 93-97.
Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
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.
Arumilli, S.V.T. (2025) ‘Maintaining Human Oversight Within Agentic Artificial Intelligence-Driven Workflows for Enterprise Operational Decision-Making and Automated Business Process Optimization Systems’, Journal of Computational Analysis and Applications, 34(10), pp. 530–538.
Mudunuri, P. R. (2024). Designing high-availability automation architectures for mission-critical research systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13864.
Devineni, A., Prabagaran, A., Mali, R. K., Mishra, M. K., Nekkalapudi, K., & Velu, R. (2026, June). Intelligent Incident Prediction in Banking Cloud Operations: A Machine Learning Framework for Proactive SRE Response. In 2026 5th International Conference on Computer Networks, Big Data and IoT (ICCBI) (pp. 1072-1078). IEEE.
Ponugoti, M. (2025). Cloud-Native Platform Engineering: Scalable Design Patterns for Global Enterprise Resilience. Journal of Computer Science and Technology Studies, 7(8), 48-59.
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.
Padmanabham, S., Khokhar, J., & Bhandari, R. S. (2025, November). Cluster Analysis and Latent Class Modelling for Consumer Segmentation in Digital Ecosystem. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
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