Cybersecurity and Audit Compliance in Blockchain and Their Implications for System Resilience and Transaction Errors

This study investigates the influence of cybersecurity indicators and audit compliance on transaction reliability and customer trust within blockchain systems. Using a dataset containing daily records of operational and security metrics, the research employs descriptive statistics, correlation analysis, and multiple linear regression to evaluate how key variables—namely security incidents, audit compliance scores, and reported cyberattacks—affect transaction errors and user trust. The analysis reveals that Security Incidents are positively correlated with Transaction Errors per Million (r = 0.64), while Audit Compliance Score (%) shows a negative correlation with transaction errors (r = -0.47) and a positive correlation with Customer Trust Index (r = 0.58). A multiple regression model indicates that approximately 68.3% of the variance in transaction errors is explained by the selected predictors (Adjusted R² = 0.683). Security Incidents are a statistically significant positive predictor (p < 0.01), and Audit Compliance Score (%) is a significant negative predictor (p < 0.05), whereas Cyber Attacks Reported show no statistically significant effect. Visual analyses further confirm these relationships: systems with higher audit compliance scores tend to exhibit fewer errors and greater user trust, while those with frequent security incidents experience higher transactional failures. These findings underscore the importance of integrating both security and audit mechanisms in blockchain risk management frameworks. Future research is recommended to incorporate additional cybersecurity dimensions and explore longitudinal trends across different blockchain architectures.