AI’s Impact on Manufacturer’s Network Security

What is Artificial Intelligence?

In today’s world AI is an increasing concern for manufacturer’s managed network security solutions. Artificial Intelligence (AI) has many definitions, depending on who you ask and what time in history you asked. Within this article (AI in Manufacturing), we define AI as having the following characteristics:

  • AI makes use of tools such as machine learning, neural networks, natural language processing, and contextual awareness
  • AI uses pre-defined algorithms to resolve problems; it does not attempt to resolve problems in the same way as the human brain
  • AI takes autonomous action based on decisions it reaches using the fundamental tools

Manufacturing can implement AI in a variety of ways. Mostly, AI is used to enhance and control various functions in the supply chain, and with physical manufacturing processes. The next section covers those aspects of AI utility in more detail.

AI-driven cybersecurity solutions are emerging in the marketplace. However, AI-driven cybersecurity services are not common at this time. This technology is in its infancy. The scope of this article does not include AI-driven cybersecurity solutions.

AI in Manufacturing

According to this definition, modern manufacturing currently implements several types of AI-driven processes.

Supply Chain:

The following elements of the supply chain can share a common database, which allows the automation of these functions.

Purchasing – Automation of inventory can be coupled with purchase order generation to ensure optimum inventory levels are maintained. This reduces the expense associated with excess inventory and reduces administrative labor costs for time spent generating and tracking purchase orders manually.

Invoicing – Manufacturer’s and buyer’s AI-driven systems can generate and distribute purchase orders and invoices automatically. Automatic invoicing requires both parties sharing data through the internet.

Inventory Control – Manufacturers can use RFID tags or other material and equipment identification tools to control inventory without the need for human intervention. This technology saves significant labor costs and enhances the accuracy of the information obtained. The inventory data is available for use by automated logistics functions and accounting functions such as purchasing and invoicing.


Shipping and Receiving – Automated inventory control systems link with shipping and receiving systems to automatically generate material load-out and receiving forms.


Customer requests for products and services automatically generate work orders. This requires manufacturers and buyers to access the same system, typically through the internet.

Automated control systems ensure precise operations. In some scenarios, AI uses measurements taken during manufacturing processes to automatically adjust machine settings and operation.

Real-time assessment of manufacturing equipment operating characteristics is used to schedule maintenance and repairs. For example, laser measurement systems can provide real-time assessment of cutter blade wear by providing accurate dimensional data. AI predicts the time of maximum wear based on dimensional changes tracked over time and production. AI then schedules maintenance or repair as required. This information is used to automatically adjust maintenance schedules. AI automatically generates internal sales orders for replacement parts using the same information.

What Cybersecurity Threats Can AI Pose to a Manufacturing Business?

AI in manufacturing poses some of the same cybersecurity threats that are present with any manufacturing facility that exchanges data and information over the internet. In some instances, AI presents a unique set of cybersecurity threats as compared to manufacturing facilities that operate without AI technology employed.

Automation of manufacturing processes requires different functions and departments to share commonly accessible data. For example, supply chain functions of invoicing, purchasing and inventory share data with logistics functions like shipping and receiving. The database that houses the shared data may be located onsite or offsite. Systems accessing the database may be internal or external. Therefore, cybersecurity risks should be assessed for the external systems as well as for the manufacturer’s network.

One example of external access would be from a buyer or vendor whose computing networks interact with the manufacturer’s network. These parties networks exchange information to generate and process sales orders, purchase orders, track movement of shipments, etc. The automated flow of information can transmit malware from a vendor’s or buyer’s network to a manufacturer’s network. Every external endpoint represents an opportunity for a malware invasion.

Consequences of AI Imposed Cybersecurity Threats

AI imposed cybersecurity threats carry the same consequences for the manufacturer as they do for any malware transmitted over the internet. However, the effective cybersecurity risk may be higher when using AI to manage external data transfer because of direct exposure to external networks. This is true because the risk is a function of consequence and probability of occurrence. In the case of manufacturing with AI, the probability of occurrence of a malware infection increases as the exposure to additional networks increases.

Effective Cybersecurity Risk Management When Using AI in Manufacturing

When AI is used in a manufacturing business, it is vitally important to understand and account for the additional exposure to cybersecurity threats. Because of the increased potential for invasion, as opposed to a business run without exposure to external computing networks, a robust cybersecurity system is vital to the security of the business. If you are contemplating on using AI in a manufacturing business, we recommend you research and implement a suitable cybersecurity plan. A competent IT MSP can help you define the best cybersecurity system to help you meet your business objectives while limiting the risk of a cybersecurity threat becoming a cybersecurity attack.

Published On: November 13, 2019Categories: CybersecurityTags: ,