AI-Driven Automation of Cybersecurity Certification Processes: Evaluating Efficiency, Transparency and Risk Mitigation in Digital Governance Systems

Abstract

This study explores how artificial intelligence (AI) can automate cybersecurity certification to improve efficiency, transparency, and governance accountability. It responds to a critical challenge in digital governance the slow, manual, and often inconsistent nature of certification processes that delay assurance and increase compliance costs. Using a multi-objective quantitative design, the research combines process mining, machine learning, and risk modeling techniques on four open-source datasets: NIST SP 800-53, MITRE ATT&CK®, CSET, and the AI Incident Database. The proposed AI-driven certification model achieved 91.4% classification accuracy (ROC-AUC = 0.941), reducing certification time by 28.3% and improving transparency by 27.2%. Risk analysis identified opacity (44.2) and bias (43.2) as the most critical governance vulnerabilities, emphasizing the need for explainable and fair AI. These results demonstrate that automation can transform cybersecurity certification into a continuous and traceable assurance process, reducing procedural delays while maintaining ethical oversight. The study highlights the policy importance of integrating AI governance standards such as NIST AI RMF (2023), ISO/IEC 42001 (2023), and the EU AI Act (2024) to ensure responsible adoption. It recommends the establishment of AI-specific audit criteria, hybrid human-AI verification mechanisms, continuous monitoring with explainable AI metrics, and mandatory ethical compliance reporting. Collectively, these measures can strengthen accountability, foster trust, and guide policymakers in building resilient and transparent digital governance systems.