A MODEL FOR THE GRADUAL ADOPTION OF ARTIFICIAL INTELLIGENCE TO STRENGTHEN CYBERSECURITY IN VENEZUELAN PETROCHEMICAL COMPLEXES

Abstract

The Venezuelan petrochemical industry, key to non-oil GDP, faces growing cybersecurity challenges from global digital threats. This research analyzes how artificial intelligence (AI) can strengthen protection in strategic industrial complexes. Through a document review and comparative analysis, the use of machine learning algorithms for real-time monitoring and anomaly detection in critical infrastructure, especially in non-modern SCADA systems, was evaluated. The findings reveal that, although AI offers valuable predictive capabilities, its implementation in Venezuela faces three main limitations: insufficient connectivity infrastructure, a shortage of specialized personnel, and budgetary restrictions. As a solution, a gradual adoption model is proposed, articulated around the following pillars: prioritizing critical systems, focusing efforts on AI implementation in areas of greatest impact and vulnerability, integrating adaptive solutions with ongoing training, combining local knowledge and international best practices, and merging the experience and understanding of the Venezuelan context with the most successful global standards and strategies. This approach would allow for the development of more robust defenses against cyberthreats, ensuring the operational continuity of vital facilities. The research emphasizes the need for strategic investments and cross-sector partnerships (industry-academia technology) to implement solutions tailored to the technical specifics of the Venezuelan petrochemical sector, ensuring its secure and sustainable digital transformation.