Cengiz Özemli
Akademisyen
- Thread Author
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Everyone is talking about what artificial intelligence (AI) has to offer, but far less is discussed about what happens when things go wrong in industrial environments where software meets steel.
As AI moves from the cloud to real-time industrial environments, the conversations around it are also changing. Factory floors, energy grids, building management systems, and distributed infrastructures are rapidly integrating AI capabilities. It's possible to automate entire workflows with predictive maintenance, process optimization, and autonomous decision-making. However, unlike chatbots that give confusing answers or systems that recommend the wrong product, AI that malfunctions in an industrial environment can lead to physical damage, system-wide failures, and threaten human safety.
### Different Risk Profile
In cloud-based AI, the consequences of failure are usually limited to poor output, slowing dashboards, or a degraded user experience. At the industrial edge, however, the risk completely changes. Three main types of concerns stand out:
- Physical safety: When AI systems interact with operational technology, misclassification or inadequately tested model updates can lead to equipment damage or harm to people.
- Cascading and systemic risk: In power grids or large-scale production facilities, poor outputs from one model can cause collapses in connected systems. This triggers a local problem to escalate into widespread and major disruptions.
- Governance and accountability gap: While errors in traditional control systems can be easily traced, identifying the causes in AI models becomes difficult. This leads to uncertainty about who is responsible.
### How Should AI Safety Be at the Industrial Edge?
The safe use of AI in industry is not about slowing down innovation, but about aligning it with the system's safety requirements. As a fundamental principle, AI should be "in the loop" rather than the "decision-maker." In most cases, AI should make recommendations; perform optimization and fault prediction; but safety brakes should remain in deterministic and certified control systems. This protects both people and equipment.
During the model development phase, version control, comprehensive testing, phased deployment, and the ability to revert to a pre-approved model if necessary are essential. At the same time, small-scale test deployments (canary deployment) should become standard practice.
Once deployed, models must be continuously monitored. Human oversight is required for automated systems and critical decisions. Emergency stop switches and safe-state defaults must be designed in advance.
### Safety Frameworks That Can Be Built Today
Although standards are not yet fully established, it is possible to create safety frameworks:
- AI should not be included in certified control loops. AI should be used for optimization and prediction, while critical safety functions should be provided by PLCs and physical safety interlocks.
- Permissible action limits, speed limits, and safe states for AI must be clearly defined. This is especially vital for autonomous AI agents.
- Change management must be ensured. This means that which model was trained by whom, when, with what data, and its settings must be clearly documented.
- AI safety should be the shared responsibility of data science, software engineering, OT engineering, and security departments. Areas of responsibility must be clearly defined.
### Standards and Regulations Are Evolving
There is currently no single comprehensive standard for industrial AI. In the safety sector, references such as ISA/IEC 62443, IEC 61508, and ISO 26262 are used. While EU AI laws and UK regulations are introducing binding regulations, ISO and IEC are also developing AI-specific standards. In the US, sector-specific regulations are preferred, but the general trend is growing, and regulations are coming.
However, companies that act before regulations arrive will not only be ready when standards come but will also remain safer until then.
### The Most Important Change: Cultural Approach
Technology alone does not solve security problems. In industry, the understanding of "moving carefully" rather than speed should be adopted. Safety and reliability should be seen as features, not restrictions. AI teams must understand that using powerful models without appropriate safety measures is a risk, not innovation.
Senior management must also inquire about AI safety and control strategies alongside their AI strategy. Those who will truly succeed are those who combine innovation with sound governance and security.
### A Clear Opportunity
The pace of AI development is very high, but the frameworks that ensure its safety are lagging. This gap represents both a risk and an opportunity. Companies and organizations that invest now have the chance to shape how global industrial AI will be used.
AI models will become even more powerful. The question is whether safety frameworks can keep up with this pace. The race in this critical area between industry and AI will be the most important race.
