INDUSTRY 4.0 & IoT
therefore be prioritised by ensuring all automated decisions are explainable through clear audit trails, while maintaining human oversight for safety- critical systems and high-consequence activities. Organisational readiness must also be assessed. This includes ensuring risks are managed scale deployment. Risks include those associated with safety, security, legality, ethics, performance and sustainability. Effective due diligence reduces the likelihood of breach occurrence and demonstrates that proportionate measures have been taken to address the risk or the consequence. All aspects along the life cycle of an AI system and its data must be covered, including data preparation, quality, model development and fairness, deployment readiness, and ongoing monitoring and feedback. The organisation must therefore be fully prepared, both technically and culturally, for widespread adoption. ISO/IEC 42001:2023 provides a structured
framework for organisations to implement and operate AI systems, analyse risks and establish protective measures. AI introduces risks that traditional IT standards aren’t equipped to handle, such as algorithmic bias or “hallucinations”, and ISO 42001 helps manage these. ISO/IEC 42001 can be easily integrated into
any existing management systems, such as the widely used quality management standard ISO 9001 or information security management standard ISO 27001. All three standards support a management system implementation that can be adapted to individual corporate structures, enabling needs-based integration into existing processes. Organisations should take concrete and effective measures for controlling and monitoring AI deployment.
controllable by prompting companies through structured risk management to address bias risks, fairness criteria, and to regularly monitor and optimise the algorithms used. is not a regulatory requirement, it is a solid compliance base to help organisations manage AI also help to secure innovations, minimise risks and increase the trust of stakeholders and customers. Proactive risk management and transparent
“Organisations must demonstrate that an AI system does not violate expected ethical principles.”
AI governance is not solely a topic for the technical department as seemingly harmless systems, such as automated decision support in procurement and inventory management, can introduce bias. This bias is where an AI system delivers systematically distorted results due to faulty or one-sided training data. An AI Management Systems (AIMS) will help to keep processes and decisions transparent and
governance are essential to building trustworthy AI systems. Complex regulatory and ethical landscapes must be successfully addressed, which is where frameworks such as ISO/IEC 42001 can help with both technical and legal compliance.
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