Domain-Tuned LLMs for Detecting Misconfigurations in Multi-Cloud Architectures

Abstract

Distributed cloud infrastructure is becoming an essential tool that the modern enterprises are utilizing to implement scalable service and application on the basis of the various service providers. Although multi-clouds lead to increased flexibility and availability of resources, they also pose a major challenge of configuration management. Systems variability in configuration representations, access controls and service dependencies between hosts also enhance the risk of infrastructure misconfiguration, resulting in security weaknesses, unauthorized access, and inconsistent operation. Turning on configuration inconsistencies of large-scale distributed infrastructures is a complicated undertaking to administrators, especially when configuration components interact with non-homogeneous environments. In an attempt to deal with these difficulties, the current paper introduces a Contextual Policy Reasoning Framework (CPRF) that is meant to be used in analyzing infrastructure setups and identifying any inconsistencies that may exist in a multi-cloud system. The suggested CPRF approach understands the configuration structures, analyses the links of infrastructure components as well as measures the dependability of configuration, the exposure of privileges, and the impact of dependencies to detect potential configuration risks. The framework also incorporates analytical modeling in order to measure configuration integrity and approximate a level of risk in infrastructure in distributed environments. CPRF allows better detecting the frameworks of complex configuration inconsistencies within heterogeneous cloud environments by thoroughly analyzing configuration links and operation dependencies. The suggested policy facilitates better configuration management and helps administrators to have uninterrupted and safe infrastructure deployments in the contemporary cloud systems. The suggested approach attains an overall configuration inconsistency detection accuracy of 96.6%, indicating enhanced dependability in the analysis of distributed cloud infrastructure configurations.