How Did the LLM Know My Information—Even I Didn’t Realize It?
Protecting Corporate Data in the Age of AI
"Just drop it into ChatGPT—it’ll summarize it in no time!"
But what if that small convenience nearly turned into a security incident that made the whole security team panic?
Five minutes later, a neatly summarized draft appears. The problem? It includes real names, phone numbers, and bank account details.
No one had bad intentions. They simply wanted to finish quickly.
AI is undeniably a powerful tool. But it also brings security concerns about how internal corporate data is handled.
Just as it's important to use AI effectively, it's now equally critical to consider how to use it safely.
Just Like the Cloud, AI Is Entering the Hybrid Era
This concern isn't new.
When cloud computing was first introduced, many companies were attracted to the efficiency and speed of public cloud services. At the same time, they were hesitant to entrust sensitive data to external systems.
Eventually, businesses adopted a hybrid cloud strategy—using both private and public cloud environments according to their needs.
Now, the same shift is happening with AI and Large Language Models (LLMs).
Public LLM vs. Private LLM—What’s the Difference?
Popular tools like ChatGPT, Gemini, Claude, and DeepSeek are examples of public LLMs.
These models are trained on massive datasets from the internet and demonstrate impressive language understanding and generation capabilities. However, since they provide services via external APIs, they pose potential risks for companies handling sensitive data.
As a result, many businesses are now actively adopting private LLMs by deploying open-source models (such as Mistral, LLaMA, or Phi) on their own servers.
Key Benefits of Private LLMs:
- Accurate and practical results: By training on internal data, the model can generate highly relevant outcomes tailored to actual business tasks.
- Enhanced security and data sovereignty: AI models are hosted within the company’s internal network, preventing data leakage and allowing full control over data used during training and inference.
- Support for air-gapped environments: These models can operate without internet access, making them suitable for high-security, closed-network systems.
- Model diversity: A wide selection of specialized models is available, including those optimized for coding, customer support, or image generation.
- Purpose-driven customization: Models can be fine-tuned for specific department needs, such as customer service, legal analysis, or technical documentation summaries.
Thanks to these advantages, more and more companies are adopting a hybrid strategy: using private LLMs for handling sensitive data and public LLMs for general-purpose information processing.
How the “Hybrid LLM” Strategy Actually Works
A hybrid strategy doesn’t simply mean “using two types of models together.”
It’s about intelligently choosing the most appropriate model depending on the sensitivity of the data and the task at hand.
For example:
By clearly separating use cases based on this kind of classification, companies can effectively balance both security and performance.
RAG: LLMs That Search the Web—and New Security Challenges
A rapidly emerging technology in this space is RAG (Retrieval-Augmented Generation).
Unlike traditional LLMs that rely solely on pre-trained knowledge, RAG-enabled models retrieve information in real time from external sources (e.g., Google, internal databases, internal wikis) to generate up-to-date and detailed responses.
However, this introduces new security risks.
If a retrieval query sent via RAG contains sensitive information like customer names, ID numbers, or bank details, it could result in a serious data breach.
How to Secure RAG Usage
To safely implement RAG, companies need to apply data anonymization and filtering processes before external searches are made.
Example Scenario:
- Original query:
- “Customer Hong Gil-dong has a credit rating of B. What is the loan interest rate for B-rated customers?”
- Secure handling:
- Preprocess the query: Remove names and identifiable information → “What is the loan interest rate for B-rated customers?”
- Perform external search
- Reprocess the search results within the internal Private LLM to generate the final answer
This structure eliminates the risk of data leakage, while still leveraging the power of external knowledge.
Conclusion: What Matters More Than Using AI Well Is Using It with Trust
AI and LLMs are already essential tools in many workflows.
But as the technology evolves, so too must the strategies we use to manage it.
Companies must now be able to answer these critical questions:
- Do we understand how AI is currently being used within our organization?
- Which types of data require the adoption of a private LLM?
- Are our security architectures robust enough when adopting advanced techniques like RAG?
- Could unintentional AI use by employees be introducing risks to the organization?
Somewhere in your organization, someone might already be using generative AI. The real question is no longer “Should we adopt it?”, but rather:
“How do we control and manage it safely?”
Are we cultivating AI as a strategic asset for the organization? Or are we unknowingly allowing it to become a hidden risk?
This article explored how to develop a secure and responsible strategy for using generative AI, especially from a data protection perspective. To harness the power of AI effectively, a responsible operational strategy must come before the technology itself.
Are we chasing convenience while leaving security behind?