Jul 2026· Journal of Reliable and Secure Computing· 0 citations
TL;DR
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.
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.
The study concludes that enterprises should treat automation and data governance as an integrated strategic agenda rather than as separate technical initiatives and recommends governance-by-design, phased implementation, workforce reskilling, periodic maturity and impact assessments, stronger model and data inventories, and independent assurance for high-risk systems.
Bisola AkejuQ, Ayokunle Olamide Ijagbemi, Shalom Alugwe· International Journal of Mul...· 0 citations
The study concludes that trustworthy digital participation depends on the integration of technical safeguards, enforceable rights, organisational culture, and transparent governance, and recommends embedding security and privacy by design, strengthening incident preparedness, improving workforce competence, enhancing regulatory cooperation, and adopting measurable accountability mechanisms.
Bisola Akeju, Shalom Alugwe, Ayokunle Olamide Ijagbemi· International Journal of Mul...· 0 citations
This study explores the integration of machine learning, data governance, and cybersecurity within next-generation enterprise data ecosystems and proposes a comprehensive framework that aligns intelligent analytics, governance policies, and security controls.
R. M· International Journal of AI,...· 0 citations
INTRODUCTION:The distributed digital economy, characterized by decentralization and cross-entity data flow, improves factor allocation efficiency but increasingly raises concerns over data security and privacy abuse.
OBJECTIVES: Unlike the conventional digital economy, which often centers on centralized platforms (e.g., e-commerce, cloud computing), the distributed digital economy in this paper specifically refers to an economic system where data—as a production factor—is stored, computed, and circulated across multiple independent nodes without a central coordinating authority, relying on technologies such as blockchain, distributed ledger, edge computing, and peer-to-peer networks. Its core governance features include decentralized data control, consensus-based verification, and peer-to-peer economic activities.
METHODS: This paper studies data security and privacy protection in the distributed digital economy from two aspects: economic impact and governance mechanism. Based on panel data from 30 provinces in China from 2018 to 2023, this paper uses the entropy weight-TOPSIS method, a two-way fixed effects model, a mediation effect model, and a spatiotemporal heterogeneity model to empirically test the economic impact and transmission mechanism of data security and privacy protection on the distributed digital economy.
RESULTS: The empirical analysis results show that the level of data security and privacy protection significantly and positively promotes the development of the distributed digital economy, with each unit increase leading to a 0.412 unit increase in the development index. Blockchain smart contracts, privacy computing standards, and cross-border data flow rules play significant mediating roles, accounting for 93.7% of the total mediating effect. This positive economic effect exhibits significant spatiotemporal differences, increasing year by year, and is significantly higher in the eastern region than in the central and western regions.
CONCLUSION: Based on empirical analysis results, optimization paths are proposed from four levels: collaborative governance, technology empowerment, regional balance, and institutional improvement, in order to improve the level of data security and privacy protection in the distributed digital economy.
Cui Wang· ICST Transactions on Scalabl...· 0 citations
Cyber threats represent a critical risk to global organisations, with cybercrime damages projected to exceed USD 10.5 trillion annually by 2025. Consequently, enterprises are rapidly adopting Artificial Intelligence (AI) to augment their security architectures, as traditional, rule based Security Information Systems (SIS) struggle to mitigate sophisticated, multi stage attacks. However, the application of AI in security raises significant ethical questions around data privacy, algorithmic transparency, and the balance between automated surveillance and civil liberties. As a conceptual paper, this study investigates how businesses can govern AI-Driven security solutions by bridging the theoretical divide between technical efficacy and ethical responsibility. To build this theoretical foundation, a systematic review of 32 peer reviewed articles was conducted using the PRISMA paradigm, synthesizing existing evidence across four core governance dimensions: technical performance, stakeholder accountability, regulatory compliance, and organisational process. Our conceptual analysis reveals a persistent "principles to practices gap"; while AI based SIS significantly outperform traditional systems in anomaly detection and incident response, these technological advancements have outpaced the operationalization of ethical norms within organisations. To address this gap, the paper proposes a novel, unified governance framework centred on digital trust. This model distinctly integrates the AI Trust Framework and Maturity Model (AI TMM), the Tiered Ethical Cybersecurity Model (TECM), and privacy preserving technologies such as federated learning to operationalize ethics by design. The article concludes with actionable policy pathways for legislators, organisational leaders, and researchers to increase cyber resilience while strictly respecting individual privacy rights.
Muhammad Faris bin Nordin, Muhammad Din bin Khalid, Normal Mat Jusoh· International journal of res...· 0 citations
This paper reviews emerging AI-GRC frameworks and regulations, including the OECD AI Principles, ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act, alongside industry standards from Microsoft, Google, and IBM, to reveal a fragmented ecosystem with overlapping principles but inconsistent enforcement and technical depth.
Abimbola Filani, J. Opoku· Magna Scientia Advanced Rese...· 0 citations