November 17, 2025

6 MIN.

AI-supported cyberattacks – new threats to production systems?

Industry 4.0, networked control systems, smart factories – these advances are changing not only the efficiency and flexibility of production facilities, but also the threat landscape. With the increasing use of AI tools, existing attack vectors in the manufacturing industry are being multiplied. The time it takes an attacker to move through a network before becoming active has been significantly reduced. This is particularly critical for production systems with legacy operating systems or open protocols, which are still quite common in the sector, and easy targets.

In this article, we will first explore how cybercriminals use AI, and then we will look at how industrial companies can counter these attacks with sustainable protection strategies.

How AI is being used to exploit existing gateways for cyberattacks

Most importantly, AI and machine learning (ML) technologies enable attackers to achieve higher hit rates with less effort. ML-supported port scanners and fingerprinting tools automate the search for machines with open protocols or known vulnerabilities. Generative AI models can create exploit scripts faster, and adapt them to individual conditions in the targeted system.

At the same time, AI takes Social Engineering to the next level. Deepfakes and high-quality phishing campaigns target production employees in the industry directly.

AI uses known vulnerabilities – instead of new ones.

In most cases, attackers do not exploit new vulnerabilities. Rather, traditional weaknesses are becoming more exposed due to AI.

Examples include:

Easy access to legacy protocols without authentication or encryption, such as SMBv1 or Telnet.

AI enables the quick and automated discovery of weak passwords or standard access data in industrial control systems.

Production facilities with older operating systems, such as Windows 10, Windows XP, or even MS-DOS control modules often lack modern security mechanisms, and can therefore be targeted at scale through automated AI-driven attack trains.

Case study: Industrial reconnaissance and access with LLMs

Let’s create a realistic scenario to illustrate the impact of AI on cyberattacks.

An attacker uses an AI agent to automatically search databases of Common Vulnerabilities and Exposures (CVE) for new vulnerabilities that meet predefined criteria.
A search engine for networked devices, such as Shodan, identifies OT devices where the identified vulnerability may not have been fixed. An exploit script tailored through prompt engineering scans the devices and attempts to penetrate the system – all within a few minutes. AI takes over the reconnaissance stage, script writing, and targeting, significantly reducing the time between identification of the vulnerability and the intrusion.

Sustainable protection strategies for the manufacturing industry in the age of AI

We rely on many proven cybersecurity principles to protect production systems from this type of threat situation. Organizations should adapt protection strategies to the unique situation of the OT environment – long machine cycles, proprietary controls, and a heterogeneous network. Resilience and risk minimization are more effective than experimental actions.

4. Resilience instead of perfection

In an industrial environment – as in any networked system – one hundred percent protection is unrealistic. For this reason, organizations must be prepared for a possible breach. Segmented networks can restrict the attackers’ movements, while incident response plans enable a swift reaction. With regular backups, systems can be fully restored after the attack has been dealt with.

Use increasing security to modernize

The increase in cyberattacks requires investment in cybersecurity, but this can be used to harness potential and drive efficiency. Connective solutions, such as edge.SHIELDOR not only secure production systems – they can reopen data sources that were assumed lost. With modern data science methods companies can gain valuable insights from these data streams to increase overall system efficiency and save energy.

If you would like to find out how TRIOVEGA’s solutions for Factory 4.0 increase the resilience of your OT infrastructure against AI-supported threats while uncovering efficiency potential, contact us to arrange a consultation.

Author: René Janz

René Janz is an industrial engineer with extensive knowledge of the shop floor. He has been with TRIOVEGA GmbH since 2023, and as Director Business Development, René is responsible for the strategic expansion of OT security and digitalization in the production industry.

Book your individual consultation here!