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Cybersecurity in machine vision applications - protection against invisible threats
Machine vision plays a crucial role in modern industrial production. It enables automated inspection, quality control and management of production processes. However, as these systems become more connected and digitized, the risk of cyber-attacks increases. The purpose of this white paper is to draw attention to this issue, highlight the current state of cybersecurity in machine vision and provide concrete recommendations for action.
Current status of cybersecurity in imaging & machine vision
Despite the central importance of machine vision in industrial applications, the topic of cybersecurity is often neglected in this area. Many of the vision systems installed today do not have a clearly defined update path or do not require regular software maintenance. As a result, outdated operating systems and software versions remain in use for many years, making them extremely vulnerable to known security threats.
Another problem is the insufficiently defined lifecycle of many systems. Without clear policies for maintenance, updates and decommissioning, security risks persist. In addition, many machine vision systems also have architectural weaknesses due to the use of customer-specific software without defined security standards or the use of easily accessible scripting languages such as Python.
Possible dangers and risks
Many companies take cybersecurity very seriously in the office environment. They deploy firewalls, antivirus programs and multifactor authentication procedures, train employees to use secure emails and downloads, and strictly monitor data traffic. However, cybersecurity is often underestimated in the production environment, especially when it comes to machine vision systems – despite the fact that there are significant risks here as well.
Industrial camera systems are used in many factories for quality assurance. These systems are often connected to other production facilities via networks but are rarely protected against cyber-attacks. While employees in the office are well trained not to open suspicious email attachments, even basic security measures are often neglected on the factory floor. For example, machine vision systems may be running outdated operating systems, may be inadequately protected or my even be connected directly to the internet, making them an easy target for attackers.
Automotive
One example is automated inspection systems in automotive manufacturing. These systems check, for example, whether body parts have been assembled correctly, or airbags work reliably. If such a system is manipulated - for example by malware that produces incorrect test results - defective products can reach the final assembly line and cause high follow-up costs.
Food
Machine vision also plays a crucial role in the food industry, for example in the detection of contamination or foreign objects during production or packaging. If attackers compromise the systems, contaminated products could enter the market undetected, posing health risks to consumers and leading to serious product recalls.
Pharma
The pharmaceutical industry uses optical inspection systems to check that drugs are correctly filled and packaged. If such a system is compromised by malware that declares faulty products as perfect, contaminated or incorrectly dosed drugs could be released into circulation, resulting in serious health consequences for patients, costly recalls and significant reputational damage.
Who is particularly affected by this topic?
They are responsible for developing secure products, as insecure hardware or software can serve as a gateway for cyber-attacks. Missing signatures, open interfaces or insecure standard configurations can be common security risks, and they should also provide regular updates.
Example: A camera manufacturer that provides firmware updates without a digital signature could unknowingly allow manipulated versions to be installed on devices, allowing attackers to manipulate data undetected.
Often the interface between manufacturers and end users, system integrators must ensure that the systems they deploy meet current security standards and are properly integrated into the existing infrastructure. In addition, the integrator is usually also responsible for the long-term maintenance of the systems.
Example: An integrator integrates vision systems into an existing production network without implementing sufficient security measures such as network segmentation or access controls. A cyberattack could then easily spread from the machine vision system to other parts of the production facility.
Companies that use machine vision systems in production should ensure the security of the systems and perform regular maintenance and updates. Managers need to be aware that the hardware or software used in production can also serve as a gateway for cyber-attacks and that attacks can result in financial loss, production downtime or faulty quality inspections. In addition, violations of cyber security policies or new regulatory requirements such as the Cyber Resilience Act can result in severe penalties.
Example: An automotive supplier relies on an automated visual inspection of components. If the system is compromised, defective parts could be passed as “OK” and go into production, resulting in costly recalls and reputational damage.
These entities could also be impacted, as they must set cybersecurity standards, monitor compliance and enforce legal requirements such as the Cyber Resilience Act and may face incidents that can affect not only businesses but also critical infrastructure.
Example: The EU Commission is introducing stricter cybersecurity standards for industrial control systems, which include machine vision. Companies that do not comply risk heavy fines or market bans for unsafe products.
Cypersecurity in machine vision - essential protection
Security in industrial image processing and machine vision is essential, even if it means additional effort and cost. In an increasingly digitized and networked production environment, inadequate security can have serious consequences. Companies that skimp on cybersecurity expose themselves to significant risks - from production downtime and financial loss to massive reputational damage. A successful cyber-attack on industrial vision systems can not only affect individual machines or production lines but also cripple entire IT infrastructures and jeopardize critical business processes.
Recognizing the growing threat, the European Union’s Cyber Resilience Act (CRA), is the first step toward a regulatory framework to ensure a minimum level of cybersecurity for all connected products on the EU market. The regulation ensures that manufacturers of digital products consider cybersecurity requirements at the design stage and provide security updates throughout the product’s life cycle. The aim of the CRA is to increase the resilience of digital products against cyber-attacks and to close security gaps at an early stage, thus minimize the risk of data leakage or manipulation.
The Cyber Resilience Act applies to all EU member states and affects a wide range of products - from simple IoT devices to complex industrial control systems, including machine vision solutions for automation and quality control. Companies operating in these areas must actively engage with the new regulations to avoid compliance risks and ensure secure systems in the long term.
After all, cybersecurity is not a one-time investment, but an ongoing process. Investing in robust security early not only protects your own infrastructure but also protects your customers and partners from potential threats - and ensures your long-term business success.
In an increasingly networked production world, the security of industrial image processing systems is of crucial importance. As an experienced and independent integrator, phil-vision supports companies in securing their machine vision systems against cyber threats and ensuring their long-term functionality. In doing so, phil-vision pursues a practice-oriented approach that ensures not only technical excellence, but also smooth integration into existing processes
Competence and experience | Thanks to years of experience in machine vision, phil-vision knows the typical vulnerabilities of such systems - from insecure network infrastructures to outdated or unsigned software. With this in-depth knowledge, we can implement targeted security measures to prevent attacks and sustainably improve the security level. |
Agile project implementations | The cybersecurity threat landscape is constantly changing. That is why phil-vision relies on flexible and agile methods to respond quickly to new requirements or potential security risks. Whether new threats emerge, or regulatory requirements change, an agile approach keeps organizations on the safe side. |
Long-term support beyond implementation | Cybersecurity is not a one-time project, but a continuous process. phil-vision therefore supports companies beyond mere commissioning and ensures that the protective measures remain effective in the long term through regular security updates, checks and adjustments. This prevents systems from becoming vulnerable over time. |
Security updates for existing systems | Many companies have relied on proven machine vision systems for years, but older installations often no longer meet current security standards. phil-vision analyses existing systems, identifies weak points and develops targeted measures to improve security - without having to replace the entire infrastructure. In this way, even older systems can be made fit for the future. |
Holistic lifecycle support | From planning and implementation to long-term maintenance, phil-vision supports companies in every phase of their project. The aim is not only to guarantee a secure initial configuration, but also to ensure the ongoing security and efficiency of the systems. This lifecycle approach keeps cybersecurity up to date, even if threats and requirements change. |
Cybersecurity in imaging and machine vision requires a holistic and long-term approach. phil-vision offers customized solutions to protect companies not only from current cyber threats, but also to prepare them for future challenges. With practical, flexible and sustainable security concepts, companies can effectively secure their machine vision systems and avoid production downtime, data loss and reputational damage.