<![CDATA[Modern enterprises increasingly rely on interconnected digital infrastructures, which significantly increase exposure to cyber threats and trust-related vulnerabilities. Traditional security mechanisms often face limitations in providing real-time detection, transparent data validation, and resilient protection across distributed environments. This study aims to develop and evaluate a Blockchain-Augmented AI Architecture to enhance cybersecurity performance and strengthen operational trust within digital business ecosystems. The research adopts a quantitative experimental approach by integrating machine learning–based anomaly detection with a private blockchain layer for secure event logging and tamper-proof verification. The system is tested using simulated enterprise network traffic, and performance is evaluated based on detection accuracy, latency, throughput, and integrity validation efficiency. A comparative analysis is also conducted against conventional centralized cybersecurity models. The results demonstrate that the proposed architecture significantly improves cybersecurity robustness, achieving higher anomaly detection accuracy, reduced false positives, and faster verification time compared to baseline models. Additionally, blockchain integration enhances operational trust by ensuring immutable audit trails, decentralized consensus, and transparent data prove- nance. Overall, the combined system exhibits superior reliability and resilience across simulated network scenarios. This study concludes that integrating AI- driven detection with blockchain technology provides a more secure, transparent, and trustworthy cybersecurity framework for modern enterprises, while of- fering strong potential to support scalable digital transformation and address emerging threats in distributed environments]]>
