A Code Red AI

As manufacturers race to adopt generative AI, they face an urgent new battleground: safeguarding data, models, and operations from AI-powered cyber threats.

- BY: Shriyal Sethumadhavan

As manufacturers adopt generative AI (genAI) to boost efficiency, they also face a new wave of sophisticated cyber threats.

Al-driven cyber risks

GenAI poses unique cybersecurity risks in manufacturing beyond traditional IT threats.

“GenAI relies on large volumes of sensitive data, increasing exposure to data poisoning, breaches, and IP theft. Attackers can manipulate training data, causing flawed outputs and disruptions,” says Scott Caveza, Sr Staff Research Engineer, Tenable. Centralised data is a prime target. Integration with IoT and cyber-physical systems broadens the attack surface, risking real-world equipment damage.

Unlike traditional cyber risks targeting infrastructure or endpoints, GenAI systems face threats to data and models, says Alexander Ivanyuk, Senior Director, Product and Technology, Acronis. “Data poisoning injects malicious data to corrupt models. Model inversion extracts confidential data or replicates IP.” Third-party reliance increases supply chain risk. These threats are critical as GenAI powers automation, predictive maintenance, and quality control.

A key threat is manipulation of algorithm-driven systems, subtly altering production specs undetected, says Gopi Sirineni, Co-Founder & CEO, Axiado. “Such attacks degrade product quality over time, causing major losses.” Input manipulations extract confidential data or bypass security, risking proprietary processes. Breaches can expose operational knowledge, enabling competitors to copy guarded methodologies.

“GenAI models rely on proprietary data – designs, production parameters, supplier info – making them prime targets for IP theft and breaches,” says Praveen Joshi, Managing Director, RSK Business Solutions. Risks include model inversion and prompt injection, where malicious inputs hijack behaviour.