Greetings from Cyberdigm!
Hottest topic across businesses worldwide right now is AX (AI Transformation). As generative AI spreads rapidly into our daily workspaces, organizations are seeking best practices for proper usages.
But the moment you try to bring AI into actual operations, another set of questions follows.
"We acknowledge that daily AI usage is critical now — but how do we use it safely, without leaking our confidential information?"
"How do we integrate AI platforms with Document Centralization environment we've already built?"
Nowadays, companies have to consider two problems at the same time: i) using AI to improve productivity and competitiveness, and ii) keeping corporate data protected and under control.
At ISEC 2026, JM Lee (DCS Sales Team Lead) and JH Son (DCS Dev Team Lead) presented "Core Strategies for Safe AI Utilization via Document Centralization Solution," addressing exactly aforementioned concerns along with real implementation cases.
In this post, we've summarized the session — how to keep corporate data under control while still making full use of AI.
Two Challenges Companies Face: Utilizing AI and Handling Data Security Threats
Core question for many companies right now focuses on: "How do we raise productivity with generative AI while keeping our core data safe?"
Data security threats existed long before AI. But as generative AI has gone mainstream, the number of paths through which data travel has grown — and so has the scope of what security teams need to cover.
① Before the AI Era: Insider Leaks and Fragmented Unstructured Data
<88% of technology leaks come from insiders>
According to Korean National Police Agency statistics on industrial technology leakage cited in the session, approximately 88% of technology leaks were caused by insiders rather than outsiders. In other words, employees with legitimate access rights are just as important a management concern as external threats.
<Unstructured data scattered everywhere>
Much of company's critical information lies in documents — business plans, price lists, contracts, customer databases, engineering drawings, HR records.
When such documents are spread across individual PCs, USB drives and external hard drives, the opportunities for leakage multiply: downloads, screen captures, printing, email, external sharing, and more.
② After the AI Era: AI Becomes a New Path for Data
Since ChatGPT arrived in 2022, generative AI has moved into the workplace at remarkable speed.
AI has become a genuinely useful tool for efficiency and productivity. On the other hand, as corporate data connects to AI in the course of daily work, entirely new data pathways have opened up — ones that didn't exist before.
As a result, the security question is no longer simply whether to permit AI use. It has become a matter of controlling which AI can access which data.
From "Complete Closure" to "Controlled Access": How Companies Have Responded, and Where the Limits Are
When generative AI first spread, some global companies restricted or banned its use outright on security grounds.
More recently, the direction has shifted. Rather than blocking AI entirely, companies are working to use it under their own security policies.
Keynote session also pointed to major global manufacturers, BigTech and financial institutions as examples of this shift — running external AI inside an enterprise security-controlled environment, or allowing AI use only through a proprietary platform.
Building an enterprise-grade generative AI environment in-house, however, requires more than high licensing costs. It also requires dedicated staff to design access permissions and operate the environment on an ongoing basis. For small and mid-sized businesses in particular, that can be a real burden.
💡 Cyberdigm's Change of Perspective
"Rather than controlling the AI itself, control the data the AI uses."
The idea is to gather corporate documents into controllable storage, apply the access rights and security policies you already have, and then connect only the AI your company has approved.
In short: utilize Document Centralization Solution as the data foundation for a Safe AI environment.
The Safe AI Architecture, Built on Document Centralization Solution
Safe AI starts with handling your company's data into a controllable state.
Instead of scattering documents across individual PCs, external drives, and USB sticks, you consolidate them through Document Centralization Solution and apply per-user permissions and security policies.
When that data connects to AI, the Safe Data Filter acts as the control layer that governs how it is used.
🛡️ The Three Core Principles of Safe AI
① Fine-grained Access Control
AI can only work with the documents a given user is allowed to access. The basic principle: if a document isn't visible to the person, it isn't visible to the AI either.
② Certified AI Only
Only AI that the company has approved connects to internal data. This keeps unvetted AI tools from reaching corporate information.
③ Data Leakage Protection
Security policies already in place through Document Centralization Solution also apply to data uploaded on AI, blocking unauthorized access and unapproved export.
How Are Companies Actually Building Safe AI?
Safe AI isn't just a conceptual model. AI environments built on document centralization solution are already running in the research, public, and financial sectors.
- Research Institute H — 1,700 users / Embedded AI Type
- Public Agency M — 3,000 users / AI Integration Type
- Bank K — 7,000 users / AI Integration Type
Depending on a company's existing IT environment and AI strategy, deployments generally take one of two forms: Embedded AI Type (Type 1) or AI Integration Type (Type 2).
TYPE 1. Embedded AI Type
In Embedded AI Type, AI function is built directly into Document Centralization Solution
Because the existing interface, folder structure, and permission model carry over as-is, an AI environment can be set up relatively quickly without complex integration work.
At H Research Institute, users ask questions in natural language within Document Centralization Solution, and AI answers within the scope of that user's permissions.
TYPE 2. AI Integration Type
If your organization already has adopted its own LLM or AI platform, the AI Integration Type will be the right fit. Customer chooses their preferred AI platform, LLM, and UI. Document Centralization Solution verifies user permissions and supplies only accessible data to the AI system.
Connections to a range of AI systems are supported through interfaces such as MCP and REST API. And if the AI model changes later, the existing document permission structure stays intact. Bank K and Public Agency M are representative cases for this type of AI usage with Document Centralization Solution.
From Built-in AI to On-Premises and Cloud AI
If Type 1 and Type 2 describe the overall shape of a Safe AI deployment, the way AI is actually connected can be broken down further according to a company's security policy and IT environment.
① Built-in AI
Package configuration with an AI Assistant included in Document Centralization Solution. Since it comes pre-optimized, speedy setup is available.
② On-Premises LLM
A private AI approach in which both document centralization solution and the LLM server are deployed on the internal network. Well suited to environments that require isolation from external networks, such as defense and finance.
③ Cloud AI API
Integration with cloud-based AI models such as ChatGPT, Claude, and Gemini.
Whichever approach you choose — built-in AI, on-premises LLM, or cloud AI — the core principle stays the same:
The company chooses the AI configuration. The data that AI uses stays controlled through Document Centralization Solution
Document Centralization Solution : Where Self-Directed Security Begins in the AI Era
As generative AI use expands, how well a company uses AI matters — but so does how safely it manages the data that AI draws on.
This ISEC session presented one answer: consolidate critical corporate data through document centralization solution, apply permissions and security, and use that as the basis for connecting the AI you need, safely.
Even in the AI era, security still starts with data.
Cyberdigm will continue building document centralization solution –based Safe AI environments that protect critical corporate data and turn it into a new asset for the AI era.
Greetings from Cyberdigm!
Hottest topic across businesses worldwide right now is AX (AI Transformation). As generative AI spreads rapidly into our daily workspaces, organizations are seeking best practices for proper usages.
But the moment you try to bring AI into actual operations, another set of questions follows.
"We acknowledge that daily AI usage is critical now — but how do we use it safely, without leaking our confidential information?"
"How do we integrate AI platforms with Document Centralization environment we've already built?"
Nowadays, companies have to consider two problems at the same time: i) using AI to improve productivity and competitiveness, and ii) keeping corporate data protected and under control.
At ISEC 2026, JM Lee (DCS Sales Team Lead) and JH Son (DCS Dev Team Lead) presented "Core Strategies for Safe AI Utilization via Document Centralization Solution," addressing exactly aforementioned concerns along with real implementation cases.
In this post, we've summarized the session — how to keep corporate data under control while still making full use of AI.
Two Challenges Companies Face: Utilizing AI and Handling Data Security Threats
Core question for many companies right now focuses on: "How do we raise productivity with generative AI while keeping our core data safe?"
Data security threats existed long before AI. But as generative AI has gone mainstream, the number of paths through which data travel has grown — and so has the scope of what security teams need to cover.
① Before the AI Era: Insider Leaks and Fragmented Unstructured Data
<88% of technology leaks come from insiders>
According to Korean National Police Agency statistics on industrial technology leakage cited in the session, approximately 88% of technology leaks were caused by insiders rather than outsiders. In other words, employees with legitimate access rights are just as important a management concern as external threats.
<Unstructured data scattered everywhere>
Much of company's critical information lies in documents — business plans, price lists, contracts, customer databases, engineering drawings, HR records.
When such documents are spread across individual PCs, USB drives and external hard drives, the opportunities for leakage multiply: downloads, screen captures, printing, email, external sharing, and more.
② After the AI Era: AI Becomes a New Path for Data
Since ChatGPT arrived in 2022, generative AI has moved into the workplace at remarkable speed.
AI has become a genuinely useful tool for efficiency and productivity. On the other hand, as corporate data connects to AI in the course of daily work, entirely new data pathways have opened up — ones that didn't exist before.
As a result, the security question is no longer simply whether to permit AI use. It has become a matter of controlling which AI can access which data.
From "Complete Closure" to "Controlled Access": How Companies Have Responded, and Where the Limits Are
When generative AI first spread, some global companies restricted or banned its use outright on security grounds.
More recently, the direction has shifted. Rather than blocking AI entirely, companies are working to use it under their own security policies.
Keynote session also pointed to major global manufacturers, BigTech and financial institutions as examples of this shift — running external AI inside an enterprise security-controlled environment, or allowing AI use only through a proprietary platform.
Building an enterprise-grade generative AI environment in-house, however, requires more than high licensing costs. It also requires dedicated staff to design access permissions and operate the environment on an ongoing basis. For small and mid-sized businesses in particular, that can be a real burden.
💡 Cyberdigm's Change of Perspective
"Rather than controlling the AI itself, control the data the AI uses."
The idea is to gather corporate documents into controllable storage, apply the access rights and security policies you already have, and then connect only the AI your company has approved.
In short: utilize Document Centralization Solution as the data foundation for a Safe AI environment.
The Safe AI Architecture, Built on Document Centralization Solution
Safe AI starts with handling your company's data into a controllable state.
Instead of scattering documents across individual PCs, external drives, and USB sticks, you consolidate them through Document Centralization Solution and apply per-user permissions and security policies.
When that data connects to AI, the Safe Data Filter acts as the control layer that governs how it is used.
🛡️ The Three Core Principles of Safe AI
① Fine-grained Access Control
AI can only work with the documents a given user is allowed to access. The basic principle: if a document isn't visible to the person, it isn't visible to the AI either.
② Certified AI Only
Only AI that the company has approved connects to internal data. This keeps unvetted AI tools from reaching corporate information.
③ Data Leakage Protection
Security policies already in place through Document Centralization Solution also apply to data uploaded on AI, blocking unauthorized access and unapproved export.
How Are Companies Actually Building Safe AI?
Safe AI isn't just a conceptual model. AI environments built on document centralization solution are already running in the research, public, and financial sectors.
Depending on a company's existing IT environment and AI strategy, deployments generally take one of two forms: Embedded AI Type (Type 1) or AI Integration Type (Type 2).
TYPE 1. Embedded AI Type
In Embedded AI Type, AI function is built directly into Document Centralization Solution
Because the existing interface, folder structure, and permission model carry over as-is, an AI environment can be set up relatively quickly without complex integration work.
At H Research Institute, users ask questions in natural language within Document Centralization Solution, and AI answers within the scope of that user's permissions.
TYPE 2. AI Integration Type
If your organization already has adopted its own LLM or AI platform, the AI Integration Type will be the right fit. Customer chooses their preferred AI platform, LLM, and UI. Document Centralization Solution verifies user permissions and supplies only accessible data to the AI system.
Connections to a range of AI systems are supported through interfaces such as MCP and REST API. And if the AI model changes later, the existing document permission structure stays intact. Bank K and Public Agency M are representative cases for this type of AI usage with Document Centralization Solution.
From Built-in AI to On-Premises and Cloud AI
If Type 1 and Type 2 describe the overall shape of a Safe AI deployment, the way AI is actually connected can be broken down further according to a company's security policy and IT environment.
① Built-in AI
Package configuration with an AI Assistant included in Document Centralization Solution. Since it comes pre-optimized, speedy setup is available.
② On-Premises LLM
A private AI approach in which both document centralization solution and the LLM server are deployed on the internal network. Well suited to environments that require isolation from external networks, such as defense and finance.
③ Cloud AI API
Integration with cloud-based AI models such as ChatGPT, Claude, and Gemini.
Whichever approach you choose — built-in AI, on-premises LLM, or cloud AI — the core principle stays the same:
The company chooses the AI configuration. The data that AI uses stays controlled through Document Centralization Solution
Document Centralization Solution : Where Self-Directed Security Begins in the AI Era
As generative AI use expands, how well a company uses AI matters — but so does how safely it manages the data that AI draws on.
This ISEC session presented one answer: consolidate critical corporate data through document centralization solution, apply permissions and security, and use that as the basis for connecting the AI you need, safely.
Even in the AI era, security still starts with data.
Cyberdigm will continue building document centralization solution –based Safe AI environments that protect critical corporate data and turn it into a new asset for the AI era.