Generative AI adoption is moving faster than anyone expected.
From document summarization and internal knowledge search to code generation and automated customer support — AI has quickly become part of our daily business operation.
But as employees begin using public AI services like ChatGPT, Claude, and Gemini at their workplaces,
companies are facing growing concerns:
“What happens to the critical and confidential data when our employees upload onto these
AI tools?”
Design files, R&D documents, customer personal information, undisclosed strategic materials,
source codes — companies are becoming increasingly concerned that company’s critical assets could unsafely
be transmitted into such AI services.
In fact, in 2023, a major global company made headlines after reports revealed that an employee had entered internal source code into a generative AI service. Since then, many organizations have started tightening or restricting internal policies around generative AI usage.
The Rising Alternative: On-Premise AI
In industries where data sensitivity is especially high — such as manufacturing, semiconductors, finance and healthcare, public sector and defense — interest in On-Premise AI as an alternative
to public AI services is growing rapidly.
(On-Premise AI refers to running AI models within company's internal infrastructure (in-house GPU servers), rather than relying on external cloud services.)
<Benefits of On-Premise AI>
- Minimized External Data Transmission
- Sensitive assets such as design drawings or strategic documents can remain inside the organization instead of being uploaded onto public AI infrastructure.
- Strengthened Data Control:
- Companies can directly manage and operate their data, gaining clearer control over where data is stored and who has access.
- With global AI services, companies often have limited visibility into where data is stored or which regulations apply.
- Regulatory Compliance
- For organizations in finance, public sector, and other industries requiring high-level security and network separation, On-Premise AI is emerging as a realistic option.
<Cloud AI vs. On-Premise AI at a Glance>
Category | Cloud AI (ChatGPT, Claude, etc.) | On-Premise AI (Self-Deployed) |
AI Model Location | External provider's server | In-house GPU server |
Data Leakage Risk | High (external transmission unavoidable) | Low (controllable internally) |
Operating Cost | No initial cost, usage-based billing | Initial deployment investment required |
Customization | Limited | Customized Training for Internal Data and Terminology |
Recommended For | Startups and general office environments where rapid deployment and collaboration are priorities | Manufacturing, financial, and public sector organizations with high level of sensitivity to data leakage |
But Is On-Premises AI Really Secure?
Here lies a critical point that many companies tend to overlook.
Simply deploying an AI model on internal servers does not automatically guarantee proper data control.
Even when external transmission is reduced, security risks still remain if organizations cannot control properly who has access to what data within.
In practice, companies adopting On-Premise AI often encounter situations like these:
Case 1) A new employee asks the AI, "Show me executive salary information," and the AI will respond directly by searching through HR documents without proper access restrictions.
Case 2) An employee preparing to resign exports AI-generated key deliverables or summaries outside the company without authorization.
Case 3) An insider queries AI for confidential assets for malicious purposes, but administrators have no way to track down such activity.
In other words, placing AI inside the company does not, on its own, prevent data leakage at the source.
What truly matters is controlling 'which documents AI can access and use to generate responses' and 'who is authorized to access specific information'.
Then How Can Companies Adopt AI Securely and Efficiently?
Linking Document-Level Access Permissions to AI
The most common approach companies first consider is integrating document-level access permissions (ACL) with AI.
For AI to generate responses aligned with each user's authority, it needs permission data on "who (User)" can access "which document (Document)."
① Integrating with Enterprise Cloud Drives (Google Drive, Microsoft 365 SharePoint, etc.)
- How It Works: Connecting the read/write permissions already configured by department in existing cloud systems with the AI's search system (RAG).
- Limitations: In reality, corporate documents are rarely stored in a single cloud environment. Maintaining consistent permission synchronization and access control across all these locations (personal PCs, NAS, messengers, and email attachments) is extremely difficult.
② Managing Document-Level Permissions Directly Within the AI System
- How It Works: After integrating the company's account system with the AI, administrators manually attach permission tags such as "HR Only" or "Executive Only" whenever a document is uploaded. The AI then checks user permissions against document tags and only retrieves documents the user is authorized to access.
- Limitations: Recently, some have attempted to assign tags automatically using AI Auto-Classification tools, but this approach is also not a complete solution. Separate training data must be prepared to train the company's confidentiality standards, and since classification accuracy is not 100%, any misclassification can lead directly to security incidents.
A Stronger Approach: Combining Document Centralization with On-Premise AI
Recently, combining a Document Centralization Solution (DCS) with On-Premise AI has emerged as a more realistic alternative.
Document Centralization Solution gathers documents scattered across personal PCs, departmental shared folders, and messengers into a central server, and supports operation under single permission framework based on position, department, and role.
- Unified Management Framework
The Document Centralization Solution enables consistent management of user permissions through organizational structure and permission policy settings.
- Real-Time Permission Synchronization
When the AI generates a response, it can be configured to search and reference only documents within the user's permission scope.
- Eliminating Security Blind Spots
Since documents are no longer stored on individual PCs and exist only on the central server, local files cannot be exposed to AI training or search without authorization.
<On-Premise AI Combined with Document Centralization Solution>
Category | Standard On-Premise AI | [Document Centralization + On-Premise AI] |
Security Focus | Blocking data transmission to external AI services | Both internal and external data control |
Data Location | Fragmented across personal PCs, messengers, etc. | Centrally managed on a unified server |
Access Permissions | Difficult to control the AI's document access scope | Linked to department/user-based permission framework |
Leakage Prevention | Difficult to block external transfer or track AI outputs afterward | External transfer can be blocked, with log-based post-tracking and management |
Data Control Is Becoming the Real AI Competitiveness
In the era of generative AI, what matters more than the AI model itself is the ability to control what data the AI operates on, how data is managed, and who is allowed to access specific information.
No matter how advanced an AI may be, if the organization doesn’t possess proper data management framework, security and operational risks will continue to grow.
A truly practical and secure enterprise AI environment can be achieved only when a Document Centralization Solution — one that consolidates internal data safely and controls it on a permission basis — is implemented alongside an AI system.
Generative AI adoption is moving faster than anyone expected.
From document summarization and internal knowledge search to code generation and automated customer support — AI has quickly become part of our daily business operation.
But as employees begin using public AI services like ChatGPT, Claude, and Gemini at their workplaces,
companies are facing growing concerns:
“What happens to the critical and confidential data when our employees upload onto these
AI tools?”
Design files, R&D documents, customer personal information, undisclosed strategic materials,
source codes — companies are becoming increasingly concerned that company’s critical assets could unsafely
be transmitted into such AI services.
In fact, in 2023, a major global company made headlines after reports revealed that an employee had entered internal source code into a generative AI service. Since then, many organizations have started tightening or restricting internal policies around generative AI usage.
The Rising Alternative: On-Premise AI
In industries where data sensitivity is especially high — such as manufacturing, semiconductors, finance and healthcare, public sector and defense — interest in On-Premise AI as an alternative
to public AI services is growing rapidly.
(On-Premise AI refers to running AI models within company's internal infrastructure (in-house GPU servers), rather than relying on external cloud services.)
<Benefits of On-Premise AI>
<Cloud AI vs. On-Premise AI at a Glance>
Category
Cloud AI
(ChatGPT, Claude, etc.)
On-Premise AI
(Self-Deployed)
AI Model Location
External provider's server
In-house GPU server
Data Leakage Risk
High (external transmission unavoidable)
Low (controllable internally)
Operating Cost
No initial cost, usage-based billing
Initial deployment investment required
Customization
Limited
Customized Training for Internal Data and Terminology
Recommended
For
Startups and general office environments where rapid deployment and collaboration are priorities
Manufacturing, financial, and public sector organizations with high level of sensitivity to data leakage
But Is On-Premises AI Really Secure?
Here lies a critical point that many companies tend to overlook.
Simply deploying an AI model on internal servers does not automatically guarantee proper data control.
Even when external transmission is reduced, security risks still remain if organizations cannot control properly who has access to what data within.
In practice, companies adopting On-Premise AI often encounter situations like these:
Case 1) A new employee asks the AI, "Show me executive salary information," and the AI will respond directly by searching through HR documents without proper access restrictions.
Case 2) An employee preparing to resign exports AI-generated key deliverables or summaries outside the company without authorization.
Case 3) An insider queries AI for confidential assets for malicious purposes, but administrators have no way to track down such activity.
In other words, placing AI inside the company does not, on its own, prevent data leakage at the source.
What truly matters is controlling 'which documents AI can access and use to generate responses' and 'who is authorized to access specific information'.
Then How Can Companies Adopt AI Securely and Efficiently?
Linking Document-Level Access Permissions to AI
The most common approach companies first consider is integrating document-level access permissions (ACL) with AI.
For AI to generate responses aligned with each user's authority, it needs permission data on "who (User)" can access "which document (Document)."
① Integrating with Enterprise Cloud Drives (Google Drive, Microsoft 365 SharePoint, etc.)
② Managing Document-Level Permissions Directly Within the AI System
A Stronger Approach: Combining Document Centralization with On-Premise AI
Recently, combining a Document Centralization Solution (DCS) with On-Premise AI has emerged as a more realistic alternative.
Document Centralization Solution gathers documents scattered across personal PCs, departmental shared folders, and messengers into a central server, and supports operation under single permission framework based on position, department, and role.
The Document Centralization Solution enables consistent management of user permissions through organizational structure and permission policy settings.
When the AI generates a response, it can be configured to search and reference only documents within the user's permission scope.
Since documents are no longer stored on individual PCs and exist only on the central server, local files cannot be exposed to AI training or search without authorization.
<On-Premise AI Combined with Document Centralization Solution>
Category
Standard On-Premise AI
[Document Centralization + On-Premise AI]
Security Focus
Blocking data transmission to external AI services
Both internal and external data control
Data Location
Fragmented across personal PCs, messengers, etc.
Centrally managed on a unified server
Access Permissions
Difficult to control the AI's document access scope
Linked to department/user-based permission framework
Leakage Prevention
Difficult to block external transfer or track AI outputs afterward
External transfer can be blocked,
with log-based post-tracking and management
Data Control Is Becoming the Real AI Competitiveness
In the era of generative AI, what matters more than the AI model itself is the ability to control what data the AI operates on, how data is managed, and who is allowed to access specific information.
No matter how advanced an AI may be, if the organization doesn’t possess proper data management framework, security and operational risks will continue to grow.
A truly practical and secure enterprise AI environment can be achieved only when a Document Centralization Solution — one that consolidates internal data safely and controls it on a permission basis — is implemented alongside an AI system.