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-/