AI systems hold great promise, but also introduce complex challenges around data protection, compliance and system integrity. Manufacturers now face a critical dilemma: how to unlock the transformative potential of AI without exposing sensitive data or opening up new vulnerabilities that could leave them open to cybersecurity pressures.
It’s where connected worker platforms can play a critical role in addressing these risks. When implemented correctly, these platforms serve as a secure foundation for AI adoption—safeguarding customer data, enforcing transparency, and embedding protection strategies that ensure AI tools support, rather than threaten, the integrity of the operation.
Cyberattacks cost millions to resolve and almost without fail, have huge implications on brand reputation, stakeholder and consumer trust, and entire supply chains. And ransomware is on the rise with industrial organizations among the most targeted , experiencing an 87 percent rise in 2024 over the previous year. With 50 percent of all observed ransomware victims in 2024 in the manufacturing sector, and 57 percent of all cyberattacks happening in North America, the industry is at the top of the hit list.
But here’s the issue. Deloitte’s 2024 Future of the Digital Customer Experience survey found that 55 percent of industrial product manufacturers are already using generative AI in their operations, with over 40 percent planning to further increase investment in AI and machine learning within the next three years. But smarter factory floors expose AI vulnerability and brings bigger attack surfaces into play.
Ransomware Attacks from All Angles
So, as manufacturers race to adopt AI, they must also prioritize robust cybersecurity strategies to protect their systems, ensure operational continuity, and maintain trust.
With today’s manufacturing facilities being more complex than ever—legacy systems are not advanced enough to fight today’s modern hacker. To make matters worse, the introduction of AI tools makes manufacturing companies more dispersed and raises a raft of new threats.
AI tools have begun to touch many facets of the manufacturing process. Whether it’s for workforce training, safety monitoring, data collection, or even AI robots on production lines down on the factory floor, the inner workings of manufacturing organizations may have become more connected and intelligent—but have also become 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. Hundreds or thousands of connected devices serve as potential entry points for hackers and sometimes, the rush to integrate AI tools have outpaced the security action plans. It’s more crucial than ever to tighten the grip on governance, compliance, and overall security in manufacturing.
Take deploying connected worker technology, for example. While AI-driven applications streamline access to crucial information, enhance global communications, and accelerate time to value with automated digital content conversion—there are key security considerations that must be addressed to protect the data that feeds these systems.
Manufacturing Data: The Breeding Ground for Hackers
Manufacturing data is highly sensitive, involving trade secrets, detailed production information, and masses of consumer data – basically a hackers dream! A critical concern when implementing AI technologies is whether manufacturing data is ever shared with external AI providers.
Again the stats tell an important story. In 2024 over 40 percent of hacking claims were because of a third-party vendor.
Customer data should not be used to train AI models and should only be processed by the SaaS 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.
Safety Should Remain Paramount
Safety and accuracy of AI outputs are paramount in manufacturing settings, where errors can lead to real-world hazards. Manufacturers should confirm that AI responses are validated for safety and correctness with outputs professionally phrased and aligned with customer-specific context.
To minimize the risk of unsafe or incorrect AI outputs in manufacturing settings, organizations should implement a layered set of guardrails and validation controls:
Content Filtering: Blocks unsafe inputs (e.g., hate speech, violence, misconduct) before reaching the model.
Prompt Injection Defense: Detects malicious prompts or attempts to exploit the system.
Few-Shot Prompting: Uses examples to guide safe and appropriate AI behavior.
Secure Handling: AI interactions occur in encrypted, access-controlled environments; no data used for training.
RAG Grounding: AI responses are based only on verified, customer-specific content; returns “No answer” if context is missing.
Bias & Scope Control: Filters outputs for bias, profanity, or off-topic content; ensures professional, on-brand language.
Human-in-the-Loop (HITL): Experts validate high-risk outputs like safety protocols or work instructions.
Multilingual & Cultural Safety: Matches input language and applies localization to ensure clarity and cultural relevance.
Adversarial Testing: Ongoing internal tests to strengthen defenses against prompt injection and other threats.
In the era of embedded AI, the burden of governance falls squarely on the SaaS provider. Customers in high-stakes environments such as manufacturing expect more than powerful features. They demand safe, compliant, and trustworthy AI. This responsibility begins with a provable foundation of security and data integrity, validated through rigorous, independent audits and adherence to industry-best practices.
However, true AI governance extends deep into the product itself. It is the provider's duty to build in the technical guardrails that ensure transparency, fairness, and alignment with established operational and safety standards. For example, systems that use Retrieval-Augmented Generation (RAG) to ground AI responses exclusively in a client's verified knowledge base, prevent dangerous "hallucinations" and ensure all outputs are contextually accurate.
Advanced connected worker platforms that prioritize data protection, enforce rigorous cybersecurity measures, and validate AI outputs for safety, fairness, and contextual accuracy, will help manufacturers confidently embrace AI innovation. AI adoption must also align with broader corporate responsibility goals—ensuring technology serves operational excellence and ethical integrity.
