AI tools, while powerful, introduce complex security and compliance challenges. Without robust, enterprise-grade security measures, the advantages of AI can quickly turn into operational disruptions and reputational damage. 

Here, we emphasize the vital role of connected worker platforms in mitigating these risks, providing a secure foundation for AI adoption. From safeguarding customer data to ensuring transparency and deploying protection strategies, these platforms are key to enabling AI-powered workforces safely and effectively. 

The AI Double-Edged Sword 

Manufacturing is now one of the most targeted industries for ransomware attacks globally, with incidents surging by 87% in 2024 compared to the previous year. Alarmingly, 50% of all documented ransomware victims have been in the manufacturing sector, and 57% of all cyberattacks occurred in North America.  

Despite growing ransomware attacks, the adoption of AI in the U.S. manufacturing industry is on the rise. According to Deloitte’s 2024 Future of the Digital Customer Experience survey, 55% of industrial product manufacturers are already using generative AI tools in their operations, and over 40% plan to expand their investments in AI and ML within the next three years. 

Cyberattacks cost millions to resolve. Given that manufacturing underpins critical industries — from automotive and aerospace to food & beverage — any cyber incident in this space can trigger widespread disruption across global supply chains, compounding risk well beyond the factory floor.  

Intelligent Factories, Exposed Systems — AI Needs Stronger Cybersecurity 

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.  

Consider deploying connected worker technology, for example. While AI-driven applications streamline access to critical 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. 

The Third-Party Risk: Safeguarding Manufacturing Data in the AI Age  

Manufacturing data is highly sensitive, involving trade secrets, detailed production information and masses of consumer data. 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% of hacking claims were because of a third-party vendor. 

Customer data should not be used to train AI models and must only be processed by the SaaS provider — never to be shared with 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. 

Avoiding Common Factory Floor Failures 

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 at Ingress: AI guardrail filters to block unsafe inputs before they reach the model — e.g., hate, insults, sexual content, violence and misconduct filters 

• Prompt Injection and Adversarial Input Detection: Inputs are pre-assessed to identify malicious intent or system prompt leaks 

• Few-Shot Prompting: Prompts include examples of acceptable/unacceptable queries to guide safe behavior 

• Secure Prompt and Response Handling: Process all AI interactions within a secure, customer-dedicated environment; encrypt logs at rest and in transit; enforce strict access controls so that prompts, responses and telemetry can be audited but never exfiltrated for model training  

• Retrieval-Augmented Generation for Output Grounding: Anchor every AI response in verified, customer-specific source content. When no relevant context exists, configure the model to return ‘no answer’ rather than risk hallucinations 

• Bias, Profanity and Scope-Drift Prevention: Include output-screening mechanisms that check for inappropriate or biased language, ensure responses remain scoped to the customer’s own data and enforce professional phrasing  

• Human-in-the-Loop (HITL) Verification: For the most critical outputs, such as safety protocols or complex work instructions, implement a workflow where a qualified human expert must review and approve the AI-generated content before it is finalized. This provides a final layer of verification, serving as the ultimate safety net to catch subtle errors or contextual nuances that automated systems might miss 

• Multilingual and Cultural Safety: Automatically match the response language to the input, and apply localization or translation when contexts differ, preserving clarity and cultural relevance 

• Purple Teaming and Internal Testing: Dedicated adversarial test suites are regularly executed to evaluate and improve prompt injection protections 

Embedding Corporate Social Responsibility   

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. 

For a provider, embracing this responsibility is a strategic mandate. Proactively 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 to drive safe, sustainable adoption and long-term operational excellence. 

Increased Potential, Increased Risk — Connected Worker Solutions Can Help 

The potential for AI integration is huge — but so are the risks. As factory environments become increasingly connected and intelligent, manufacturers and their technology partners need to implement the right safeguards and enterprise-grade security precautions to address cybersecurity threats, data privacy concerns and ethical challenges. Connected worker technology is ready and waiting.