Beyond Logs: Building Intelligent Monitoring Systems with Splunk Clusters
DOI:
https://doi.org/10.64137/3107-9458/ICCSEMTI26-119Keywords:
Splunk Clusters, Intelligent Monitoring, Observability, Log Analytics, SIEM, DevOps, Distributed Systems, SRE, AIOpsAbstract
Modern businesses go above and beyond just recording information and addressing problems as they arise. When collecting data from a large number of workstations, centralized logging solutions shine. However, when it comes to distributed, cloud-based, and microservices-based systems, they may not hold up as well. The proliferation of hybrid clouds, containers, and edge systems has led to a dramatic increase in the volume, velocity, and diversity of operational data. A system's security, speed, and reliability should not be assumed only because it offers static dashboards and keyword-based searches. Interaction between smart monitoring systems is essential for their ability to collect, measure, and store data in real time. They should also have the ability to anticipate problems and address them promptly. It is necessary to make this modification in Splunk clusters. The multi-site architectures, indexer clustering, and search head clustering that make up Splunk's business observability solutions make them very dependable and error-tolerant. In this post, we'll look at how Splunk clusters may help businesses create robust monitoring systems that can do more than just centralize log storage. Topics covered include automated frameworks, AI-driven operational insights, clustering approaches, scalable architecture, and advanced analytics.
References
[1] Thallapally, Nagaraju. "How to Build and Maintain a Powerful Logging and Monitoring System." Journal of Electrical Systems 21 (2025).
[2] Shelke, Palvi, and Tapio Frantti. "Exploring the possibilities of splunk enterprise security in advanced cyber threat detection." The Proceedings of the... International Conference on Cyber Warfare and Security. Academic Conferences International Ltd, 2025.
[3] Zadrozny, Peter, and Raghu Kodali. Big data analytics using Splunk: Deriving operational intelligence from social media, machine data, existing data warehouses, and other real-time streaming sources. Apress, 2013.
[4] Bumgarner, Vincent. Implementing Splunk-Big Data Reporting and Development for Operational Intelligence. Packt Publishing Ltd, 2013.
[5] Clemente, Davide António Melo. "Real-time failure prediction in distributed systems via log analysis: A proof of concept." (2025).
[6] Kaarrela, Jani. "Developing a cybersecurity monitoring dashboard in Splunk." (2025).
[7] Aare, Chandrashekar Reddy. "Scalable SIEM Architectures for Global Enterprises: Engineering Real-Time Visibility with Splunk." Journal Of Engineering And Computer Sciences 4.8 (2025): 291-298.
[8] Paredes Barreda, Antoni. "Deploying a distributed splunk architecture with log ingestion for SIEM." (2025).
[9] Miller, James D., et al. Improving Your Splunk Skills: Leverage the operational intelligence capabilities of Splunk to unlock new hidden business insights. Packt Publishing Ltd, 2019.
[10] Skopik, Florian, Markus Wurzenberger, and Max Landauer. Smart Log Data Analytics. Springer International Publishing, 2021.
[11] Nyman, Jonathan. "Splunk Dashboard Development for Elisa Navitas." (2025).
[12] Aitiddir, Hajar, and Noureddine Kerzazi. "Cloud Infrastructure Monitoring Using Splunk: Expectations and Challenges." 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2023.
[13] Guduru, Sandhya. "AI-Enhanced Threat Detection Graph Convolutional Networks (GCNs) for Zeek Log Analysis in Splunk ES." Journal of Scientific and Engineering Research 10.8 (2023): 166-173.
[14] Yarushev, Sergey, and Aleksandr Anurov. "Modern Methods for Anomaly Detection in Enterprise System Logs: Algorithms, Implementations, and Practical Case Studies." International Workshop on Advanced Information Security Management and Applications. Cham: Springer Nature Switzerland, 2025.
[15] Marlette, Travis. Splunk Best Practices. Packt Publishing Ltd, 2016.
[16] Reddy, R. P. (2024). A survey of distributed denial of service (ddos) attack mitigation techniques. International Journal of Computer Trends and Technology (IJCTT), 72(12), 69-77.
[17] PellReddy, R. (2024). Empowering cloud security: Pioneering an interactive multi-factor authentication framework for cloud user verification.
[18] Vemula, V. R. (2025).AI-Powered Framework for Proactive Monitoring of Dark Web Marketplaces and Prediction of Emergent Cybercrime Trends.
[19] Nidamanuri, S., Tirumalasetty, P., Kilari, N. S., & Lu, J. (2023). MSI-Multi-Step Interaction Networks for Spatial-Temporal Forecasting. IJSAT-International Journal on Science and Technology, 14(2).
[20] Gali, V. K., & Eruvuru, B. K. (2023). AI-Assisted Continuous Controls Monitoring (CCM) in Oracle Cloud ERP: An Intelligent and Adaptive Framework for Enterprise Compliance. International Journal of AI, BigData, Computational and Management Studies, 4(4), 138-146. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P115
