New Darktrace report spotlights growing cybersecurity risks manufacturers face from AI
New Darktrace report spotlights growing cybersecurity risks manufacturers face from AI
Publish Date: 2026-06-08 11:11:00
Source Domain: www.smartindustry.com
Using an unordered list, summarize the following article with between 4 and 8 key points. When implementing agents, Cox-Robinson said, data determines how well AI models are trained. Bad AI models yield bad results in operational efficiency and quality control, which is a risk that manufacturers need to deal with, he said.
“When you then have things like generative AI, which then increases the potential for data to be leaked. I think that may be one of the reasons why manufacturing security leaders are better able to understand the risks of AI adoption above other industry verticals,” he said.
Visibility concerns and solutions
Addressing the AI implementation challenge, according to Cox-Robinson, requires a different approach to security that can operate at the same speed and scale as AI and named three priorities for manufacturing organizations: visibility, context and guardrails.
Generally, he said, risks start from a lack of visibility of AI agents within manufacturing organizations.
Having visibility of agents includes knowing where the agents are physically hosted and used; who is using them, from both human-to-agent and agent-to-agent identities; and monitoring prompts used in generative AI agents.
Visibility, he explained, allows risks to be quantified so real-time detection and policies can then be put in place.
“You can actually monitor the how people are using it and potentially abusing it, either maliciously or accidentally, in terms of how they might be accidentally exposing risk, which allows you to then kind of perform that real-time detection [while] still allowing them to do what they’re really good at, but putting guardrails in place in terms of real time detection,” he said.
See also: How much more proof do you need that cybersecurity should be essential to your plant?
In terms of context, detecting threats requires understanding patterns within the organization and identifying deviations as they happen. This is then supported by guardrails, in which organizations need boundaries around actions they can take with these agents which need to be embedded into systems themselves, Cox-Robinson said.
Additionally, many manufacturers still depend on legacy security tooling that isn’t always able to keep up with speed of attacks as it is dependent on known bad actors. Risks can increase, he said, just by simply adopting AI within businesses.
“This will require a new generation of AI-powered security tools,” he said. “That doesn’t mean the problem is that different as a starting point, but it does mean a shift in mindset of how we solve it.”
