Data Governance and Policy Support for Secure AI-Driven Corporate Digital Transformation

Abstract

Artificial intelligence is becoming a core engine of corporate digital transformation, but its value depends first on secure, reliable, and accountable data and model infrastructures. As firms combine cloud platforms, edge devices, IoT sensors, digital twins, platform data, and algorithmic decision systems, they also expand the attack surface, privacy exposure, model security risk, and compliance burden. This paper develops a security-aware data and AI governance framework for AI-driven corporate digital transformation. It positions the framework as a unified governance model rather than a narrow extension of data management: data governance controls data classification, provenance, access, privacy, and sharing, while AI governance assures model validation, robustness, auditability, and accountability. The paper identifies six dilemmas: data sharing versus protection, weak provenance and pipeline security, adversarial or opaque AI models, vulnerabilities in cloud-edge-IoT and digital-twin ecosystems, unequal compliance capacity between large firms and SMEs, and fragmented coordination across cybersecurity, privacy, competition, and industrial policy. It then proposes an integrated agenda of tiered data governance, zero-trust and encryption-based security, privacy-enhancing collaboration, model validation and adversarial testing, algorithmic audit, incident response, regulatory sandboxes, certification, public secure data spaces, maturity indicators, and SME-oriented compliance services. The study contributes to reliable and secure computing research by showing that technical controls, organizational routines, and policy support must be integrated to enable trustworthy AI-driven transformation across firms of different sizes and sectors.