The integration of AI in manufacturing has and will continue to drive major gains in efficiency, productivity, and cost savings. But without enterprise-grade security, these benefits can quickly turn into serious operational and reputational risks.
Industrial organizations across the world remain prime ransomware targets, facing an 87% year-over-year increase in 2024. Manufacturing remains a top target, with the industry accounting for half of all observed ransomware victims and 57% of cyberattacks occurring in North America. As manufacturing underpins industries like automotive, aerospace, and food & beverage, any successful cyberattack has ripple effects across supply chains, exacerbating disruption and threatening the integrity of multiple markets.
Dispersed AI entry points increase vulnerability
Today’s legacy systems are not advanced enough to fight today’s modern hacker, and the introduction of AI (artificial intelligence) tools makes manufacturing companies more dispersed, raising a raft of new threats. Whether it’s workforce training, safety monitoring, data collection, or even AI robots on production lines down on the factory floor, the inner workings of manufacturing organizations have become more connected and intelligent—but more vulnerable.
Now, as AI-powered workforce operations rely heavily on data, sensors, and networks, the attack surface for cyber hackers and threats has only given them more opportunities.
Keep AI processing locked, secure, and compliant
Manufacturing data is highly sensitive, involving trade secrets, detailed production information, and masses of consumer data, which should never be shared with external AI providers.
Customer data should not be used to train AI models and should only be processed by the SaaS (software-as-a-service) provider—never sent to external AI model providers. All inputs, outputs, and embeddings must remain sealed within secure infrastructure—operated, monitored, and audited by the SaaS provider to guarantee full data sovereignty, privacy, and compliance. Advanced connected worker platforms address this by processing all data within secure environments such as AWS and complying with strict data residency laws. With prompts and responses also processed entirely within the AWS environment, it enables manufacturers to tap into powerful AI functionalities on the factory floor, while maintaining strict privacy, control, and compliance.
Ensuring AI responses are correct and fit for purpose
Safety and accuracy of AI outputs are paramount in manufacturing settings, where errors can lead to real-world hazards. Manufacturers should confirm AI responses are validated for safety and correctness with outputs professionally phrased and align with customer-specific context. To minimize the risk of unsafe or incorrect AI outputs in, manufacturers should implement a layered set of validation controls, such as content filtering at ingress or prompt injection and adversarial input detection identifies malicious intent or system prompt leaks.
AI governance is a strategic mandate for SaaS providers
In the era of embedded AI, governance rests squarely with the SaaS provider. Customers in high-stakes industries expect safe, compliant, trustworthy AI, built on proven security and data integrity validated by independent audits.
However, true AI governance goes beyond just security. Technical guardrails ensure transparency, fairness, and alignment with established operational and safety standards. For example, systems that use RAG (retrieval-augmented generation) to ground AI responses exclusively in a client’s verified knowledge base, prevent dangerous hallucinations and ensure all outputs are contextually accurate.
For a provider, this is a strategic mandate. Embedding ethical controls and robust governance transforms a product from a simple tool into a trusted, strategic asset. By doing so, SaaS providers not only mitigate their customers’ legal and reputational risks but also build the essential trust needed.
Building a safer, smarter future for AI in manufacturing
Moving forward, it is clear manufacturers must implement strong cybersecurity measures in order to safeguard the growing amounts of essential data and validate AI’s growing role in business for safety and fairness. Secure connected worker technologies are in the perfect position to prioritize data security and enforce robust cybersecurity protocols. Only then will manufacturers be able to unlock AI’s full potential with confidence.
About the author:
Serge Thibault, VP Information Security at Poka, https://www.linkedin.com/company/poka-inc-/