Secure Distributed Data Processing Using Privacy-Preserving Artificial Intelligence and Zero Trust Architecture for Enterprise Risk Identification and Performance Evaluation

Abstract

This study examined the problem of increasing privacy, security, and governance risks in enterprise distributed data processing environments where sensitive operational, customer, compliance, and security data are processed across cloud platforms, enterprise applications, remote systems, and third-party infrastructures. The purpose of the study was to assess how privacy-preserving artificial intelligence and Zero Trust Architecture strengthen secure distributed data processing for enterprise risk identification and performance evaluation. A quantitative, cross-sectional, case-based research design was adopted, focusing on cloud and enterprise cases involving distributed data systems, cybersecurity tools, AI-supported analytics, compliance systems, and digital governance practices. The sample consisted of 214 valid respondents selected from IT and system professionals, cybersecurity professionals, data and analytics professionals, compliance and risk officers, managers, and digital transformation officers, with a usable response rate of 85.6%. The key variables included privacy-preserving artificial intelligence, Zero Trust Architecture, secure distributed data processing, enterprise risk identification, and enterprise performance evaluation. The analysis plan used descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, regression analysis, readiness index assessment, and a secure distributed data processing trustworthiness matrix. The findings showed high mean scores for all major constructs, including Zero Trust Architecture, M = 4.14, secure distributed data processing, M = 4.11, privacy-preserving AI, M = 4.08, enterprise risk identification, M = 4.06, and enterprise performance evaluation, M = 4.02. Reliability was strong, with Cronbach’s alpha values ranging from 0.854 to 0.891 and an overall scale reliability of 0.914. Correlation results showed that Zero Trust Architecture had a strong relationship with secure distributed data processing, r = 0.721, p < 0.001, while secure distributed data processing was strongly related to enterprise risk identification, r = 0.697, p < 0.001. Regression results confirmed that Zero Trust Architecture significantly predicted secure distributed data processing, β = 0.442, p < 0.001, and secure distributed data processing significantly predicted enterprise risk identification, β = 0.514, p < 0.001. The combined model explained 58.4% of enterprise security performance variance. The study implies that enterprises should integrate privacy-aware AI, Zero Trust controls, access governance, anomaly detection, and auditability into distributed data systems to improve risk visibility, compliance, resilience, and performance evaluation.