<![CDATA[This research investigates the integration of Artificial Intelligence (AI) and blockchain technologies to develop a more robust and adaptive cybersecurity framework. Amid the growing complexity and frequency of cyber threats, traditional security systems are increasingly insufficient in ensuring data integrity, threat detection, and operational transparency. The study aims to explore how the synergy between AI and blockchain can address these limitations and enhance digital security infrastructures. A qualitative exploratory approach was employed, utilizing a Systematic Literature Review (SLR) of 42 peer-reviewed articles published between 2020 and 2025. The analysis revealed three dominant integration models: AI-based anomaly detection with blockchain-secured logging, smart contracts for automated incident response, and blockchain-based identity verification enhanced by AI behavioral analysis. The proposed framework demonstrated a high detection rate (94.3%), low response latency (0.7 seconds), and improved auditability compared to state-of-the-art approaches. These findings suggest that combining AI's predictive capabilities with blockchain’s immutable and decentralized architecture offers a more comprehensive cybersecurity solution. However, challenges such as computational overhead, energy consumption, and interoperability issues remain. The study concludes that the integrated approach not only enhances resilience and transparency but also provides a scalable foundation for future cybersecurity systems, especially in critical sectors such as healthcare, finance, and government services.]]>
