Foundations of Trust: Navigating AI’s Reliability (a four-part series) 

Part 3: Data Privacy and Security 

If you’re like me, you try to minimize sharing personal identifiable information. It’s not that I distrust everyone, it’s just that I understand that there are so many conflicted advisors that feel sharing your information is justifiable.    

In some cases, the information you share may be needed to complete their task at hand, and in those cases, we should do some research and determine if the person, or company, can be considered a trusted advisor.  Yet, when you are building Generative AI into your solution, or integrating with AI solutions, you must take care to protect the data in transit, and the data at rest.

Exposing the information or losing protection/control of the data in your enterprise would be akin to a clerk asking you to write your credit card information on a piece of paper to complete a transaction.  Even if they said they would destroy it after the transaction, I would still have some security concerns, and wonder if I should treat that person as a trusted advisor.

Data Privacy & Security

Just as we carefully consider what we share with those around us, protecting data privacy in AI solutions is even more crucial. When Generative AI is embedded into business processes, it’s vital to take extra measures to safeguard your competitive edge and prevent sensitive information from being exposed.

Knowing the pros and cons of public versus private LLMs empowers you to create a solution that serves as a reliable and trusted advisor. Think of a private LLM as an advisor who has signed a non-disclosure agreement, guaranteeing that your interactions remain confidential and only the information needed to solve the task is shared.

Rocket Software supports businesses by enabling AI models to run securely within their own data environments, reducing the need to move sensitive data externally. This “bring the model to the data” approach preserves data sovereignty and helps organizations meet regulatory standards, all while enhancing protection by integrating AI workloads closely with core enterprise systems.

Learn more about Rocket Software’s data modernization and security solutions at rocketsoftware.com/solutions/data-modernization.

And for those instances where data must be transferred to and from datastores within a Generative AI solution, Rocket Software provides secure and reliable methods to safeguard sensitive information throughout the entire process. Leveraging Rocket API integration, organizations can control and encrypt data access, ensuring that only authorized requests are fulfilled. Meanwhile, our Rocket Data Replication (RDRS) solution enables fast, consistent, and secure data movement between systems, minimizing risk during transfer. Together, these tools help ensure that only the necessary data is shared, maintaining strong security and compliance without sacrificing performance.

Choosing between public and private large language models (LLMs) requires careful consideration of your organization’s unique needs, especially when it comes to safeguarding data privacy and maintaining security. Just as we’ve highlighted, private LLMs offer a level of confidentiality akin to trusted advisors bound by strict agreements, while public models may require additional safeguards to protect sensitive information. Striking the right balance ensures your AI solution not only drives innovation but also upholds the integrity and trust your business depends on. Stay tuned!  In the final article of the series, we’ll look at Use Case Fit and Cost, helping organizations determine when an AI solution’s value justifies investment, and how to align AI deployments with strategic goals to maximize return on investment while minimizing risks.

For more information about Rocket Software, and the products, solutions and services we have to help you add AI to your Enterprise’s solution, please visit rocketsoftware.com.