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The Dragos Blog

05.12.23 | 1 min read

Leveraging AI in Manufacturing: Enhancing Efficiency, Quality, and Safety 

Michael W. Sakmar

Manufacturers are rapidly adopting artificial intelligence (AI) to enhance their production processes and improve their bottom line. AI-driven technologies such as predictive maintenance, quality control, inventory management, supply chain management, and autonomous robots are transforming manufacturing by improving efficiency, quality, and safety.

As manufacturers increase their ability to leverage AI, they also face cybersecurity risks that need to be addressed. In this blog, we will explore these risks and recommend ways to mitigate them effectively.

Cybersecurity Risks Associated with Implementing Industrial AI in Manufacturing

One of the primary cybersecurity risks associated with implementing industrial AI in manufacturing and production processes is external network connections to the AI model. In most cases, the AI model is hosted in the cloud, and manufacturers establish external network connections from their facility to the AI model, creating a potential vulnerability for cyber attacks.

In 2022, Dragos found that 83 percent of manufacturing clients had undocumented or uncontrolled external connections to their OT process environments. External connections are one of the top threat behaviors used by ransomware groups. The most effective security control for reducing the cyber risks associated with remote access remains multi-factor authentication (MFA). However, it is not feasible to implement MFA everywhere for every situation.

Mitigating Risks and Protecting Sensitive Data

To mitigate cybersecurity risks associated with implementing industrial AI in manufacturing and production processes, manufacturers should take the following steps:

  1. Limit the number of different remote access vendors, products, and solutions in an environment. A recent threat hunt found that most manufacturing facilities had at least nine different solutions.
  2. Avoid always-active remote connections. Instead, implement them as available upon request and then utilize monitoring to ensure they are only used when authorized.
  3. Ensure the ability to rapidly disconnect external connections. This is essential for effective incident response.

Manufacturing facilities generate a vast amount of sensitive data, which must be protected from unauthorized access and data breaches. Enterprises should anonymize this data as much as possible before sending it to industrial AI systems. To address unauthorized access, MFA and the three recommendations above should be considered.

Leveraging AI in manufacturing is transforming production processes and boosting bottom lines, but with these benefits come cybersecurity risks that must be addressed. Manufacturers must take a proactive approach to mitigate these risks and ensure the security and privacy of sensitive data generated by industrial AI systems. By following the recommendations outlined here, manufacturers can harness the power of AI while protecting their assets and increasing their long-term security.

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