AI-Driven Continuous Security Validation for Enterprise Linux Systems Using Configuration-as-Code

Abstract

Enterprise Linux systems form the backbone of modern financial, healthcare, and large-scale enterprise infrastructures. These environments are required to meet strict security and compliance standards while supporting continuous operational availability. Traditional security validation approaches for Linux systems rely heavily on periodic vulnerability scans, manual audits, and reactive remediation processes, which often fail to detect configuration drift and emerging security risks in a timely manner. As system scale and complexity increase, these limitations become more pronounced, leading to delayed remediation and increased exposure to compliance violations.

This paper presents an AI-driven approach for continuous security validation of enterprise Linux systems using Configuration-as-Code principles. By representing security baselines, hardening standards, and compliance controls as version-controlled configurations, the proposed approach enables consistent enforcement and validation across large Linux environments. Artificial intelligence techniques are applied to analyze configuration deviations, identify recurring misconfigurations, and prioritize remediation efforts based on risk and operational impact. Rather than replacing existing security tools, the approach augments them by providing continuous assessment and intelligent decision support.

Through architectural analysis and practical observations from enterprise Linux environments, this study demonstrates how integrating AI-assisted analysis with Configuration-as-Code improves visibility, reduces configuration drift, and strengthens overall security posture. The findings suggest that continuous, automated validation can significantly enhance compliance readiness and operational resilience while reducing manual effort in large-scale Linux infrastructures.