What question did this study set out to answer?

The aim is to introduce a framework for integrating AI into critical infrastructure systems, specifically addressing legacy system challenges.

May 10, 2026Open Access

Implementing Machine Learning for Enhanced Critical Infrastructure Protection: A Framework-Centric Approach for Legacy Systems

Key Points

The aim is to introduce a framework for integrating AI into critical infrastructure systems, specifically addressing legacy system challenges.
Presented a comprehensive framework for AI deployment in critical infrastructure.
Conducted a pilot study to explore integration challenges faced by legacy systems.
Addressed technical, regulatory, and operational hurdles in the integration process.
The framework facilitated smoother transitions to AI-enhanced security systems.
Integration challenges were clearly identified and addressed, leading to improved maintenance and security.
Suggestions for future advancements in critical infrastructure protection were outlined.

Abstract

This article introduces a comprehensive framework for integrating Artificial Intelligence (AI) into critical infrastructure security systems, with a focus on overcoming integration challenges with legacy systems. It details the necessity of a structured approach to AI deployment, addressing technical, regulatory, and operational hurdles to enhance security and maintenance. Through the lens of a pilot study, the paper discusses the encountered integration challenges, underscoring the framework's role in facilitating a smoother transition to AI-enhanced infrastructure. This work lays a foundation for future advancements in critical infrastructure protection through AI.

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