In the face of increasing cyberattacks and a persistent shortage of skilled labour, the use of artificial intelligence (AI) can make manufacturing companies more resilient and future-oriented. However, the increasing networking of industrial plants - in the context of operational technology (OT) - also increases the risk of network-related production downtime and cyberattacks. While security measures have been established for IT systems over decades, the security level of OT systems is often inadequate. The combination of outdated operating systems and control components as well as increasing networking pressure means that industrial systems are becoming increasingly vulnerable to cyberattacks and conventional security measures are no longer sufficient to effectively ward off threats. This jeopardises not only the IT infrastructure, but also production quality and operational continuity.
AI as a virtual security employee
The lack of qualified specialists is an increasing challenge for the operation of cyber security structures. AI-based solutions offer a concrete approach to alleviating this: they automate routine tasks, relieve the burden on security teams and free up time for complex and strategically relevant tasks.
A central field of application for AI is the automated recognition of networked systems. For company management, this means complete transparency across critical IT and OT structures. Based on the communication between machines and systems, AI can create an almost complete and always up-to-date overview of the IT and OT infrastructure - an essential step, as many companies only have incomplete or outdated system documentation.
By continuously analysing these communication patterns, AI identifies key information about the existing systems, such as manufacturers, software and communication relationships. This creates a centralised and continuously updated asset inventory. At the same time, the AI recognises potential security gaps and evaluates which countermeasures promise the greatest security gain.
Another area of application is the real-time detection of anomalies in network traffic. The AI can learn the normal operating behaviour and detect suspicious deviations in real time that indicate cyber attacks or internal security incidents.
AI against advanced threats
Companies are not the only ones relying on AI - cyber criminals are also using machine learning and AI techniques to optimise their attacks. Automated malware generation, AI-driven phishing campaigns and adaptive attack strategies are making conventional security approaches increasingly ineffective.
Especially in industrial environments, where cyberattacks can cause not only financial but also physical damage, a forward-looking security strategy is essential. AI-based cybersecurity plays a crucial role here: by using self-learning algorithms, the system can adapt to new threats in real time, identify attacks in the planning phase and recognise previously unknown attack patterns.
Success factors for use in cybersecurity
The successful use of AI - and therefore also an increase in efficiency and maturity in cyber security - depends largely on the right framework conditions. As described above, AI is playing an increasingly central role, particularly in the areas of asset detection and visualisation, risk assessment and anomaly identification. However, it can only realise its full potential under certain conditions:
Data quality and availability
AI models require high-quality data in order to reliably recognise systems and threats. To do this, security logs, network and sensor data should be recorded comprehensively and continuously.
Integration into existing security structures
The use of AI and associated tools requires clear responsibilities and regular system checks. Without structural anchoring, there is a risk that the resulting benefits will remain unutilised, systems will become obsolete and wrong decisions will be made.
Governance
The growing demands on cybersecurity and the shortage of qualified specialists make automation unavoidable. AI can help companies to recognise threats more efficiently, respond to them more quickly and strengthen their resilience. The right balance is crucial: those who view AI as an intelligent supplement and not as the sole solution will be more successful in the long term. Companies that specifically promote the use of AI-supported security solutions create the conditions for a higher level of protection - a worthwhile investment in security and quality.
Conclusion: AI against increasing cyber threats
The growing demands on cybersecurity and the shortage of qualified specialists make automation unavoidable. AI can help companies to recognise threats more efficiently, respond to them more quickly and strengthen their resilience. The right balance is crucial: those who view AI as an intelligent supplement and not as the sole solution will be more successful in the long term. Companies that specifically promote the use of AI-supported security solutions create the conditions for a higher level of protection - a worthwhile investment in security and quality.
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