AI in the Digital Transformation of Technical Protection Systems: The SAFEGUARD Framework and Pilot Study

This is the authors' abstract. We don't add key points for this paper.

The protection of critical infrastructure is increasingly challenged by evolving cyber threats and sophisticated attack methods. Traditional technical security systems, such as access control, alarm systems, and power supply monitors, often rely on outdated technologies and isolated data, limiting their ability to address modern risks or provide predictive insights. The SAFEGUARD project addresses these limitations by integrating Artificial Intelligence (AI) and Machine Learning (ML) into the digital transformation of security infrastructure. This paper presents a strategic framework for AI/ML adoption in critical infrastructure protection, including a 10-point plan for secure implementation. It explores the use of machine learning, natural language processing, and deep learning to enhance threat detection and system resilience, while also acknowledging associated risks and implementation challenges. A pilot study demonstrates how combining data from legacy systems (e.g., UPS, access control, CCTV, HVAC) with AI models can enable predictive analytics, improving detection of security incidents and operational anomalies. The results indicate enhanced risk awareness, faster incident response, and proactive maintenance capabilities. The paper concludes with key insights and outlines future directions for scaling the SAFEGUARD approach.