izvorni znanstveni rad
Ensuring strategic resilience in critical infrastructures supported with a machine learning
approach requires moving beyond compliance checklists and post-incident analysis toward
proactive, intelligence-based approaches. This study introduces the Forensic Resilience
Operational Model (FROM), a systems thinking framework designed to embed forensic
intelligence into the resilience cycle of complex socio-technical systems. To quantify this
integration, the study investigates the determinants of the extent to which four operational
pillars (forensic readiness, anomaly detection, governance and privacy safeguards, and
structured intelligence integration) affect forensic resilience, using empirical survey data
from 212 cybersecurity professionals across critical infrastructure sectors. We deploy Partial
Least Squares Structural Equation Modelling (PLS-SEM) to investigate these relationships,
and the results confirm that anomaly detection is the strongest contributor to forensic resilience, followed by structured intelligence integration and forensic readiness. Governance
safeguards, while comparatively weaker, provide the necessary legitimacy and assurance
of compliance. Supported with sector-specific case studies in the maritime, financial, and
CERT domains, the findings highlight both the adaptability of the proposed FROM and
the operational constraints encountered in real-world contexts. The study contributes to
the field of systems-oriented strategic management by demonstrating that, when systematically embedded, forensic intelligence enhances adaptive capacity, supports predictive
decision-making, and strengthens resilience in environments characterized by uncertainty
and high complexity.
anomaly detection; digital forensics; forensic readiness; resilience modeling; critical infrastructure; cyber resilience; governance safeguards; predictive analytics; cyberphysical systems; strategic adaptation
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