How RAG Makes AI Safe for Proprietary and Regulated Manufacturing Data

3 min read ● Silk Team

Companies recognize the benefits of AI and want to make full use of it; however, most do not fully implement AI technology due to concerns about risk. This is not a question of whether AI will be effective or useful — the issue is related to the sensitivity of the types of information companies have. Information that is classified as proprietary, regulated, includes engineering documentation, compliance requirements, customer information, and intellectual property, when mishandled or miscommunicated in a regulated environment can have severe ramifications.

In this context, Retrieval-Augmented Generation (RAG) is an important innovation for manufacturing organizations. RAG provides companies with the ability to utilize AI in a productive manner and still maintain complete control over their proprietary and regulated content.

The Failure of Generalized AI Technology

Generalized AI systems are typically developed utilizing large, open-source public datasets. Although generalized AI systems provide great capabilities, they present many challenges to manufacturing companies.

• Generalized AI systems are capable of generating responses that contain inaccurate or outdated information.
• There is no assurance that the responses generated by generalized AI systems are compliant with internal company policies or regulations.
• If the prompts or responses provided by generalized AI systems are not properly managed, there is a possibility that sensitive information will be disclosed.
• Hallucination can result in significant compliance or safety issues for organizations operating under regulatory frameworks, including ISO, FDA, etc.

These risks cannot be tolerated by organizations working under compliance-based regulatory frameworks.

The Difference Between RAG and Generalized AI Systems

RAG differs from generalized AI systems in how RAG develops responses. Rather than providing answers based upon what the language model “knows,” RAG restricts responses to only that information that a company has explicitly authorized.

RAG operates in two stages:

• Retrieval: Retrieves relevant content from internal proprietary content locations, i.e. SOPs, engineering documentation, quality manuals, regulatory submissions, etc.
• Generation: Utilizing the retrieved content, the language model develops a response.

If the requested information does not exist in the approved content location, the system is unable to generate an answer.

Protection of Proprietary Knowledge

Many manufacturing companies express concerns that the use of AI will inadvertently expose internal knowledge or proprietary design information. RAG minimizes this risk by:

• Maintaining proprietary documents within controlled environments;
• Restricting the development of models from accessing or storing proprietary content;
• Restricting the output of models to authorized user-defined document sets.

Engineers, operators, and support personnel receive accurate answers to their queries without disclosing designs, formulas, or other proprietary information outside of the company.

Support of Regulatory and Compliance-Based Operations

RAG is particularly beneficial to organizations operating in a regulated environment, as it facilitates:

• Traceability: Links generated responses to the source document(s) or section(s);
• Version Control: Ensures responses remain aligned with the most current version of the approved documents;
• Audit Readiness: Facilitates demonstration of origin of information.

Regulatory, Quality, and Legal departments gain confidence in the reliability of AI-generated responses because they are explainable — not based upon guessing.

Increased Safety of AI Use Across Teams

RAG enables companies to extend the availability of AI across teams without increasing the risk associated with the use of AI. Maintenance, operations, customer service, and sales teams can all use AI to query internal knowledge bases safely.

Reduced reliance on Subject Matter Experts and ensured consistency among teams, based upon the same approved information base.

Governance without Stifling Innovation

Perhaps one of the greatest advantages of RAG is its ability to provide balance. RAG enables companies to:

• Implement Data Access Controls;
• Establish document sets that are available to teams;
• Update content centrally as regulatory or standards requirements change;
• Development of AI capabilities become a managed process rather than a wild experiment.

Getting Started for Manufacturing Companies

Most organizations follow a phased implementation plan for RAG:

• Begin with a well-defined set of documents (i.e. SOPs, quality manuals, etc.)
• Assign strict access permissions;
• Pilot test with a narrow scope;
• Validate the accuracy, traceability, and compliance of results.

Phase-in of RAG within the organization helps build confidence with both internal stakeholders and external regulatory agencies.

Conclusion

RAG allows companies to leverage the benefits of AI technology without risking safety, compliance, or proprietary knowledge. By limiting AI responses to only approved, secured content, companies can increase productivity while maintaining control.

For companies that operate in a regulated environment, RAG is not just a way to enhance AI capabilities — it is the foundation that supports the adoption of AI responsibly, efficiently, and effectively.