— In an age of increasing digital threats and data complexity, organizations need to adopt integrated security frameworks that leverage the analytical capabilities of Artificial Intelligence (AI) alongside the immutability of Blockchain. This study examines how AIdriven cybersecurity enhances anomaly detection, predictive threat modeling, and automated response mechanisms to address advanced persistent threats and internal vulnerabilities. Machine learning techniques surpass traditional systems by enabling real-time monitoring, behavioral analytics, and rapid risk assessments across enterprise networks. At the same time, blockchain's decentralized architecture and cryptographic strength provide tamper-proof data storage, secure access management, and regulatory compliance, especially in privacy-sensitive sectors like healthcare. The paper highlights the practical applications of blockchain in securing electronic health records, facilitating traceable supply chain operations, and supporting reliable digital forensic investigations. Additionally, it proposes a synergistic AI-blockchain ecosystem capable of delivering autonomous, transparent, and resilient cybersecurity solutions. Through detailed use cases and architectural insights, this work showcases the transformative potential of merging AI and blockchain to redefine cybersecurity protocols, enhance privacy, and maintain data integrity across digital infrastructures.
Penubelli et al. (Sun,) studied this question.