AI-Powered Governance-as-Code for Secure Linux Operations in FinTech Infrastructure

Abstract

FinTech infrastructures operate under strict requirements for confidentiality, integrity, availability, and regulatory compliance. Enterprise Linux systems commonly host payment processing services, customer-facing APIs, data pipelines, and backend platforms that must remain secure while supporting continuous operational change. Traditional security governance approaches for Linux operations often rely on periodic audits, manual reviews, and static policy enforcement, which can struggle to keep pace with rapid deployments, configuration drift, and evolving threat conditions. As infrastructure scale increases, ensuring consistent governance across heterogeneous Linux fleets becomes challenging, frequently leading to delayed detection of misconfigurations and inconsistent compliance evidence.

This paper proposes an AI-powered Governance-as-Code framework for secure Linux operations in FinTech environments. The approach represents governance policies, security controls, and operational guardrails as version-controlled, testable code artifacts. Continuous evaluation validates runtime system state against governance definitions, while AI-assisted analysis identifies recurring governance failures, prioritizes risks based on operational and regulatory impact, and reduces noise from low-value findings. The framework is designed to augment established security and operations workflows rather than replace them, preserving human oversight and auditability.

Through architectural design and controlled evaluation in enterprise Linux settings aligned with FinTech operational patterns, this study demonstrates how AI-assisted Governance-as-Code improves control consistency, reduces configuration drift, and strengthens compliance readiness. The findings suggest that combining continuous validation with adaptive, explainable analysis can enhance secure Linux operations and governance effectiveness in regulated FinTech infrastructures.