Risk Assessments Under the Framework of ISO 12100

18 Nov 2025

Making Industrial Products Smarter without Compromising Safety

Product safety is no longer just about sturdy electrical and mechanical design, it’s about intelligence, data and advanced risk management. Considering the rise of artificial intelligence (AI) and machine learning (ML), companies are rethinking how they approach risk assessment for complex machinery. And at the heart of this transformation lies ISO 12100, the global standard guiding risk assessment and reduction in industrial environments.

In this blog, we explore how AI and ML technologies can reshape each phase of the ISO 12100 risk assessment framework, making industrial products smarter without compromising safety, and more compliant.

ISO 12100 provides the principles of risk assessment and risk reduction for machinery throughout its lifecycle. The three key concepts of risk assessment are outlined below and AI and ML can significantly enhance all of them.

1. Hazard Identification
2. Risk Estimation
3. Risk Reduction

Faster and Smarter Identification of Hazards with AI

Hazard identification is the fundamental and basic part of a risk assessment. Hazards are identified depending on the expertise of people involved in the risk assessment process and review of multiple designs and documents. The process is especially time-intensive and poses a risk of failing to identify all potential hazards. AI could change that.

• Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP can analyze maintenance logs, incident reports, user manuals and other technical documents to flag recurring safety issues that might otherwise be missed.
• Smart vision systems in combination with 3D vision, AR/VR inspection tools, and deep learning object detection can detect potential hazards like pinch points, sharp edges, ergonomic hazards, rotating parts, etc., by automatically scanning the CAD models of complex machines and detect potential hazards and hazardous zones.

Risk Estimation Powered by ML

ISO 12100 requires a structured risk estimation based on the parameters below:

• Severity of harm
• Probability of the occurrence of harm
• Exposure
• Occurrence
• Possibility to avoid

ML models can analyze large data sets like historical incident data, sensor logs, failure records, incident reports and even environmental conditions to predict these risk factors with higher accuracy.

With IoT-enabled components, evaluating risk dynamically during machine operation becomes reality and adjusting risk level in real time when the operating condition change is possible. This helps in having proper risk assessment throughout the entire lifecycle of the machine.

Tools like reinforced learning (RL) and digital twins can simulate hazards to variable severity level for example the risk of injury under different machine speeds, guarding setups, or operator behaviors and accurately estimate the risk under different environments.

Lifecycle Management and Risk Assessment in Real Time

The concept of safety of machinery considers the ability of a machine to perform its intended functions during its life cycle where risk has been adequately reduced. Risk assessment is an ongoing activity especially when the machinery is modified or used over time when wear out starts. AI and ML can turn the machinery into a self-aware safety system.

AI can continuously monitor equipment / components via connected systems like ones with IoT to detect abnormalities in the machinery. The real time monitoring provided by AI can generate alarms as and when a fault is identified in the system and under certain conditions it can even predict the failure before it happens and becomes a potential risk to the operator.

AI in Risk Reduction

Risk reduction is a process that can be necessary to eliminate hazards as far as practicable and to adequately reduce risks by the implementation of protective measures. The objective of risk reduction can be achieved by the elimination of hazards, or by separately or simultaneously reducing severity and probability of occurrence that determine the associated risk.

AI tools like design optimization algorithms and generative design tools use machine learning to explore alternative and optimized machine designs that can minimize or eliminate hazards. Thus, helping the designers to implement inherent safety measures.

Digital twins can create a real-time model of complex machinery to test the safety performance under various environment conditions without being manufactured or installed. Real-time risk identification could help safety instrumented systems to take correct actions before a failure occurs.

Smart safety guards and adaptive controls can detect human interaction and reduce/adjust the parameters like speed, force, torque, range, etc., to stay within safe limits.

Information for Use

Drafting information for use is an integral part of the design of a machine. Section 6.4 of ISO 12100 has an extensive list of requirements for information to be provided with the machinery. The new Machinery Regulation 2023/1230 allows digital Instructions for use.

AI can considerably increase relevance, accuracy, and clarity in the documentation. Intelligent documentation tools can generate customized safety instructions based on specific configurations. Training and onboarding using virtual models powered by AI will be more effective than traditional training methods.

Relevance monitoring and data mismatch tracking are an added advantage when using AI tools in preparing information for use. AI in the background can provide a simplified interface for the risk assessment tool which will make the risk assessment process very effective.

Automatic update of instructions, especially the ones related to maintenance using predictive maintenance techniques, can help save a huge amount of machine down time since it is based on real time wear and tear data from the components. Extracting key information from risk assessment like residual risks, conditions of which the user needs to be warned, assumptions made in the risk assessment are performed effectively without human interaction.

Great advantages Come with Certain Limitations

The core of AI is data quality. When the data sets used to train the AI are incomplete or corrupt, it can lead to inaccurate risk predictions. The black box model of AI makes it difficult to justify its decision making to auditors.

The best approach is the hybrid method to use AI as an augmenting tool and not as a replacement for technical judgement.

Final Thoughts

The integration of AI and ML into risk assessment represents a massive step forward in speed, accuracy, and proactive safety management. When paired with the structured, proven method of ISO 12100, these technologies offer a smarter path to safer machines.

If you're designing industrial equipment, managing safety systems, or preparing for a compliance audit, now’s the time to explore how intelligent tools can support your work and engage with your certification partner.