Fundamentos Teóricos Para Sustentar Un Modelo Preventivo En La Ciberseguridad Financiera

Abstract

This study analyzes the theoretical foundations underpinning a preventive model in financial cybersecurity, with the aim of understanding how conceptual approaches to big data, machine learning, and technological risk management can enhance the anticipation of threats in the financial system. The study was conducted using a qualitative, documentary approach, involving a systematic review of recent scientific literature and Colombian regulations related to digital security and operational risk. The results show that the rise of financial digitization has increased the complexity of cyberattacks, limiting the effectiveness of traditional security models. Recent research indicates that the use of artificial intelligence and big data analytics enables the detection of anomalous patterns and improves the ability to respond to emerging threats(Alrafi & Mishra, 2024). In this regard, the reviewed theoretical foundations demonstrate that preventive models must be based on adaptive systems, real-time analysis, and regulatory compliance in order to ensure stability and trust in digital financial environments. It is concluded that the integration of these approaches provides the conceptual foundation necessary for the development of preventive models aimed at risk anticipation, thereby contributing to the strengthening of cybersecurity in the Colombian financial sector.