Autonomous Compliance Governance for Linux Infrastructure Using AI-Based Control Models

Abstract

Enterprise Linux infrastructure operates under strict regulatory, security, and operational governance requirements. Ensuring continuous compliance across large and distributed Linux environments remains a persistent challenge due to system scale, configuration drift, and frequent operational changes. Traditional compliance governance approaches rely on periodic audits, static control checks, and manual remediation processes, which often fail to provide timely visibility into compliance violations and emerging risks. This paper proposes an autonomous compliance governance framework for Linux infrastructure using AI-based control models. The framework represents compliance controls and governance policies as declarative artifacts and continuously evaluates runtime system states against these controls. Artificial intelligence techniques are applied to model control behavior, analyze deviation patterns, and adapt compliance validation based on system context and historical trends. Rather than enforcing rigid rule-based checks alone, the proposed approach enables adaptive governance that prioritizes high-risk violations while maintaining transparency and auditability. Through architectural analysis and controlled evaluation in enterprise Linux environments, the study demonstrates that AI-assisted compliance governance improves detection accuracy, reduces recurring compliance violations, and enhances operational efficiency. The findings suggest that autonomous governance models can strengthen regulatory adherence and resilience while reducing the manual effort traditionally associated with Linux compliance management.