STATISTICAL ANALYSIS OF AI SECURITY METRICS AND ORGANIZATIONAL COMPLIANCE WITH DATA PROTECTION STANDARDS

As artificial intelligence (AI) becomes increasingly integrated into cybersecurity systems, assessing its performance in relation to data protection compliance has become a critical area of study. This research investigates the statistical relationship between AI-based security performance metrics and organizational compliance with data protection standards, focusing on frameworks such as the Nigeria Data Protection Regulation (NDPR) and the General Data Protection Regulation (GDPR). Using Canonical Correlation Analysis (CCA), the study examines multivariate data collected from a sample of organizations across key sectors including finance, education, and healthcare. AI security performance was measured through indicators such as detection accuracy, false positive rate, and response time, while compliance was assessed through audit scores, policy implementation levels, and employee awareness training. The analysis reveals statistically significant associations between AI performance and compliance outcomes, suggesting that organizations with higher data protection compliance tend to also exhibit more effective AI-based security operations. These findings support the hypothesis that regulatory alignment may enhance institutional cybersecurity maturity. The study contributes to the emerging field of regulatory-driven cybersecurity research and offers practical implications for policymakers, data protection officers, and IT security professionals seeking to optimize both AI systems and regulatory compliance frameworks. The paper concludes by recommending the integration of statistical monitoring tools for continuous assessment of AI performance in relation to evolving regulatory requirements.