Paper Details
AI Enhanced Configuration Management Preventing System Misconfigurations
Authors
Perumallapalli Ravikumar
Abstract
Configuration management has become a major challenge for system administrators because to the growing complexity and scale of distributed systems, especially in cloud environments. System disruptions, performance deterioration, and serious security risks can result from misconfigurations. The dynamic nature and scale of contemporary infrastructures can make traditional configuration management techniques inadequate, particularly when dealing with hostile or unreliable networks. In order to improve configuration management procedures and provide a proactive strategy for preventing system misconfigurations, this article investigates the integration of artificial intelligence (AI).
The system can automatically identify, modify, and optimize setups in real-time, guaranteeing security and performance, by utilizing machine learning algorithms and AI-driven decision-making frameworks. Anomaly detection, predictive analytics, and adaptive configurations are important techniques that are often overlooked when dealing with quiet misconfigurations. Based on recent developments in AI for system administration and cybersecurity, this study emphasizes how AI may automate and improve configuration management procedures in cloud-based and dispersed settings. In order to improve security posture, the method also investigates how AI might be included into a zero-trust architecture.
The study concludes with suggestions for further study and real-world implementation of AI-based configuration management, addressing issues including data privacy, interpretability, and the requirement for domain-specific models.
Keywords
Cloud Infrastructure, Cybersecurity, Machine Learning, Anomaly Detection, AI, Configuration Management, System Misconfigurations, Zero-Trust Architecture.
Citation
AI Enhanced Configuration Management Preventing System Misconfigurations. Perumallapalli Ravikumar. 2016. IJIRCT, Volume 2, Issue 1. Pages 1-11. https://www.ijirct.org/viewPaper.php?paperId=2412036