In light of increasing cyberattacks and a persistent shortage of skilled workers, the use of artificial intelligence (AI) can help manufacturing companies become more resilient and future-oriented. However, the ongoing 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 pressure to connect, means that industrial systems are becoming increasingly vulnerable to cyberattacks and conventional security measures are no longer sufficient to effectively ward off threats. This jeopardizes not only the IT infrastructure, but also production quality and operational continuity.
AI as a virtual security employee
The shortage of qualified specialists poses a growing challenge for the operation of cybersecurity structures. AI-based solutions offer a concrete approach to alleviating this problem: they automate routine tasks, relieve security teams, and create space for complex and strategically relevant tasks.
A key area of application for AI is the automated detection of networked systems. For corporate management, this means complete transparency over critical IT and OT structures. Based on communication between machines and systems, AI can create a nearly complete and constantly updated overview of the IT and OT infrastructure—an essential step, as many companies only have incomplete or outdated system documentation.
By continuously analyzing these communication patterns, AI identifies essential information about existing systems, such as manufacturers, software, and communication relationships. This creates a centralized and continuously updated asset inventory. At the same time, AI detects potential security gaps and evaluates which countermeasures promise the greatest security gains.
Another area of application is the real-time detection of anomalies in network traffic. AI can learn normal operating behavior and detect suspicious deviations in real time that indicate cyberattacks or internal security incidents.
Alexander Hauke
Senior Manager, Consulting
KPMG AG Wirtschaftsprüfungsgesellschaft
AI against advanced threats
It's not just companies that rely on AI – cybercriminals also use machine learning and AI techniques to optimize their attacks. Automated malware generation, AI-driven phishing campaigns, and adaptive attack strategies are making traditional security approaches increasingly ineffective.
A proactive security strategy is essential, especially in industrial environments where cyberattacks can cause not only financial damage but also physical damage. 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 as early as the planning phase, and detect previously unknown attack patterns.
Success factors for use in cybersecurity
The successful use of AI—and thus also an increase in efficiency and maturity in cybersecurity—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 visualization, risk assessment, and anomaly identification. However, it can only reach its full potential under certain conditions:
Data quality and availability
AI models require high-quality data in order to reliably detect systems and threats. To this end, security logs, network and sensor data should be collected comprehensively and continuously.
Integration into existing security structures
AI should be seen as a support for the security team, not a replacement. A combination of machine intelligence and human expertise significantly increases the effectiveness of protective measures.
Governance
The use of AI and related tools requires clear responsibilities and regular system reviews. Without structural anchoring, there is a risk that the benefits gained will remain unused, systems will become obsolete, and wrong decisions will be made.
Conclusion: AI against increasing cyber threats
The growing demands on cybersecurity and the shortage of qualified specialists make automation inevitable. AI can help companies detect threats more efficiently, respond to them more quickly, and strengthen their resilience. The key is to strike the right balance: those who view AI as an intelligent supplement rather than a standalone solution will be more successful in the long term. Companies that specifically promote the use of AI-supported security solutions are laying the foundations for a higher level of protection – a worthwhile investment in security and quality.
