Governance Challenges in Autonomous Machine-to-Machine Communication: A Systematic Literature Review

(M2M) communication within Industry 4.0 environments. The growing adoption of the Industrial Internet of Things (IIoT), cyber-physical systems (CPS), and intelligent software agents has enabled higher levels of industrial autonomy while introducing new governance challenges associated with security, trust, accountability, interoperability, transparency, and responsible autonomous decision-making. Following the PRISMA 2020 methodology, this review synthesizes peer-reviewed studies published between 2010 and 2026 across multiple research domains, including industrial systems, artificial intelligence, cybersecurity, semantic technologies, and information systems. The review examines existing governance approaches, the technologies that support them, implementation capabilities, persistent challenges, and areas requiring further investigation. The findings indicate that governance research is distributed across several interconnected domains, including AI governance, Industrial IoT governance, data governance, trust management, semantic interoperability, explainable AI, and cybersecurity. Although considerable progress has been achieved in areas such as authentication, access control, secure communication, semantic representation, and AI transparency, the available evidence remains fragmented. The review also identifies several recurring limitations, including limited validation in real industrial environments, the absence of standardized governance evaluation frameworks, restricted interoperability across heterogeneous systems, and insufficient accountability mechanisms for autonomous decision-making. Overall, the review shows that current research provides a solid basis for advancing governance in autonomous industrial environments. At the same time, it highlights the need for integrated governance frameworks, rigorous empirical validation in industrial settings, standardized evaluation methodologies, stronger interoperability, and more trustworthy governance mechanisms to support autonomous industrial systems. Keywords: Autonomous systems; Machine-to-machine communication; Industry 4.0; Industrial IoT; AI governance; data governance; explainable AI; knowledge graphs; trust management, and systematic literature review.