Bolstering Cybersecurity and Blockchain Networks Through AI Technologies

Abstract

The rapid evolution of digital technologies has increased cybersecurity challenges. This situation necessitates integrating intelligent systems capable of adaptive threat detection, automated defense mechanisms, and sustainable resilience. This study explores the role of Artificial Intelligence (AI) technologies in optimizing threat detection, enhancing network resilience, and automating cybersecurity frameworks, with a specific focus on their impact on maintaining the integrity of blockchain protocols within Nigeria’s digital infrastructure. The paper investigates the application of various AI techniques, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Graph Neural Networks (GNNs), Reinforcement Learning (RL), Adversarial Machine Learning (AML), Federated Learning (FL), and Explainable AI (XAI), in strengthening cybersecurity operations. The methodology employed a qualitative narrative review approach along with a conceptual framework design. The theoretical framework is based on the Technology-Organization-Environment (TOE) framework, offering a comprehensive perspective on how technological innovations are adopted in organizational and national contexts. The findings indicate that AI-driven models significantly improve threat detection accuracy by identifying anomalies, predicting intrusions, and enabling real-time mitigation of cyber risks. In the realm of blockchain security, AI aids in the verification of smart contracts, data authenticity, and regulatory compliance, elements that are critical for maintaining integrity across Nigeria’s financial, energy, and public administration sectors.