Security Technology

Secure AI Technology for Government and Enterprise

Connecting AI is only half the challenge. Securing it is what makes it enterprise-ready.

For governments, public institutions, and enterprises, AI adoption is not simply about choosing the most capable model.

The real questions are:

Where is sensitive data processed? Who can access it? What information leaves the organization? What systems can AI access or control? And how can every action be governed and audited?

This is where secure enterprise AI begins.

YEJIN designs Security-by-Design AI architectures built around AI orchestration, Private sLMs, Local RAG, On-Premise AI, and Multi-Brain cross-model validation.

On-Premise
Closed Network
Private sLM
Local RAG
Access Control
Audit & Logging
Multi-Brain Validation

Explore the seven security layers

AI SECURITY

AI adoption begins with Data Sovereignty

Most public generative AI services process prompts, documents, and contextual data through external cloud infrastructure or third-party AI providers.

That may be convenient for general-purpose use.

For governments and enterprises, however, the security requirements are fundamentally different.

Administrative records, personal information, confidential business documents, engineering files, contracts, R&D data, ERP and CRM records, source code, and intellectual property cannot always be transferred to external AI services.

YEJIN enables organizations to bring AI processing closer to where their data already resides through a Private AI Architecture.

Your Data. Your Network. Your AI. AI should operate where your data is supposed to remain.

PUBLIC SECTOR SECURITY

Secure Private AI for Government & Public Sector

Government agencies, public institutions, municipalities, and public research organizations require security architectures that go beyond conventional public AI SaaS.

YEJIN designs AI environments according to each organization's:

Network topology · Data sensitivity · Security policies · AI use cases · External connectivity requirements

01. Internal Network AI

AI models and servers can be deployed inside an organization's internal environment so that search, retrieval, inference, generation, and document processing take place within infrastructure controlled by the institution.

Depending on security requirements, external commercial LLM APIs can be restricted or removed entirely.

Typical use cases

Administrative document search
Internal regulations and policy search
Legal and regulatory assistance
Knowledge management
Report drafting
Internal AI assistants
Public-sector RAG services
Workflow support agents

Core Architecture

Closed Network Network Segmentation On-Premise AI Server Private sLM Local RAG / Vector Database Internal Database Integration Restricted or No External AI API

02. Internal–External Hybrid AI

Some organizations need the capabilities of external foundation models or public information sources while maintaining strict control over sensitive institutional data.

YEJIN can separate internal processing areas from approved external AI services.

Sensitive information remains inside the controlled environment, while only approved information is allowed to pass through governed integration channels.

Core Controls

Internal and external processing-zone separation
API Gateway-based control
Approved outbound communication routes
Controlled data transfer
Sensitive-data filtering
Service-specific access policies
External model allowlisting

03. Citizen-Facing AI

AI services for citizen support, welfare assistance, public administration, and information services require additional separation between public-facing interfaces and internal administrative systems.

YEJIN designs architectures in which public AI services do not receive unrestricted access to internal systems.

Instead, only approved:

Datasets · APIs · Knowledge Bases · Business Functions

are exposed through controlled interfaces.

04. Private Cloud & Hybrid AI

Organizations do not always need to choose between 100% cloud and 100% On-Premise.

YEJIN can design hybrid architectures combining:

On-Premise infrastructure
Private Cloud
Secure Cloud
Private sLMs
Open-source models
Approved commercial LLMs

Sensitive and non-sensitive workloads can be separated according to security level, operational requirements, model performance, and total cost of ownership.

PUBLIC AI SECURITY

Public-sector AI should be designed around security from the beginning.

YEJIN approaches public-sector AI as an information-security architecture project — not simply as an AI installation project.

Depending on the institution and project scope, the architecture can be designed to address requirements across the following areas.

Network Security — Network segmentation, firewall policy, internal and external zone separation, controlled communication paths, and closed-network operation.
Identity & Access Management — Authentication, authorization, role-based access control, privileged access management, and user lifecycle controls.
Data Protection — Data classification, minimization, masking, encryption, retention, deletion, and sensitive-information controls.
Logging & Auditability — System logs, administrator activity, AI usage history, access records, and security-event monitoring.
AI Model Security — Controls addressing model access, prompt injection, unauthorized model usage, sensitive-output exposure, and excessive agent permissions.
Operational Security — Vulnerability management, security patches, backup, recovery, incident response, and continuous monitoring.

Applicable public-sector security requirements are reviewed on a project-by-project basis according to the organization's security policy, data classification, infrastructure, and deployment model.

ENTERPRISE SECURITY

Your most valuable business data does not have to leave your organization.

For enterprises, one of the greatest concerns surrounding generative AI is not model performance.

It is data exposure.

Customer information, contracts, pricing, financial records, source code, manufacturing data, engineering documents, ERP information, and proprietary know-how may become security risks when transferred to external AI environments without adequate controls.

YEJIN builds Enterprise Private AI environments that allow organizations to utilize AI while maintaining control over sensitive information.

MANUFACTURING

Protect your industrial intelligence.

Manufacturers can apply AI to:

Engineering drawings
BOM data
Manufacturing procedures
Quality documentation
Production records
MES data
ERP data
Inventory information
Maintenance records

while keeping critical industrial knowledge within customer-controlled infrastructure.

AI environments can be segmented across production networks, business networks, R&D environments, and external services according to customer security requirements.

FINANCE · LEGAL · PROFESSIONAL SERVICES

Intelligence without compromising confidentiality.

Private AI can support sensitive content including:

Contracts
Financial information
Customer records
Legal documents
Compliance materials
Internal regulations
Confidential reports

Private RAG and Private sLM architectures allow organizations to apply AI while keeping sensitive knowledge within a controlled environment.

Access can be restricted by:

Department · Role · Position · Data Classification · Business Function

RETAIL · DISTRIBUTION · SERVICES

AI Agents can be connected to:

CRM
POS
Inventory systems
Order management
Procurement
Logistics
Customer support

while maintaining internal control over customer and operational data.

R&D · TECHNOLOGY COMPANIES

Keep intellectual property private.

Source code, patents, engineering documents, research data, algorithms, and proprietary know-how can be searched, analyzed, summarized, and utilized through a private AI environment.

Organizations can benefit from generative AI without necessarily transferring their core intellectual property to external AI platforms.

YEJIN SECURITY ARCHITECTURE

Security is not a single feature. It is a layered architecture.

YEJIN approaches AI security through seven security layers.

NETWORK SECURITY

Control where AI can communicate.

AI servers, databases, applications, agents, and external services should not communicate without explicit policy.

YEJIN designs network architectures based on environments such as:

Closed Networks
Network Segmentation
Internal Networks
DMZ
Private Cloud
Secure API Zones

When external communication is necessary, communication paths can be restricted to approved destinations, systems, services, and purposes.

DATA SECURITY

Protect the most important AI asset: your data.

AI security begins with the data used by AI.

YEJIN's Private AI architecture is designed to keep sensitive information within infrastructure controlled by the customer.

Through Local RAG and internal Vector Databases, organizations can build AI knowledge systems without necessarily storing confidential documents on external AI platforms.

Security policies can include:

Data classification
Data minimization
Masking
De-identification
Encryption
Retention policies
Deletion policies
Controlled export

Keep enterprise knowledge under enterprise control.

IDENTITY & ACCESS SECURITY

AI should never have unlimited access.

Enterprise AI must respect organizational access boundaries.

YEJIN can design access structures based on:

User identity
Department
Job function
Role
Data classification
Application privileges

Administrators, developers, operators, and end users can be assigned different levels of access.

AI access to data should follow the same security principles as human access to data.

MODEL & AI SECURITY

AI creates a new class of security risks.

AI systems face threats that conventional enterprise applications were not originally designed to address.

These may include:

Prompt Injection
Data Poisoning
Unauthorized Model Access
Sensitive Information Disclosure
Excessive Agent Privileges
Malicious Tool Execution
Insecure Model APIs
Uncontrolled External LLM Usage

YEJIN designs AI security controls according to the risk profile and operational context of each project.

Private sLM deployment also enables organizations to reduce their dependency on external model providers.

APPLICATION & API SECURITY

Securing the model alone is not enough.

Modern AI environments connect many systems:

AI Models Web Applications Mobile Applications APIs Databases AI Agents ERP CRM Groupware Enterprise Applications

YEJIN designs these components as part of one integrated security architecture.

Depending on the implementation, controls may include:

API authentication
Service authorization
Network policies
Secret management
Container isolation
Service-level privilege separation
Controlled system integration

MONITORING & AUDIT

Critical AI activity should be traceable.

Organizations need visibility into:

Who accessed the AI system
What data was accessed
Which model was used
Which tools or applications were invoked
What administrative actions occurred
Whether abnormal behavior was detected

YEJIN can design logging and monitoring structures aligned with customer governance requirements.

Operational security may include:

Monitoring · Backup · Recovery · Patch Management · Incident Response · Audit Logs

AI TRUST SECURITY

Security should protect decision quality, too.

AI security is not only about unauthorized access or data leakage.

Incorrect AI outputs can also create:

Operational Risk Legal Risk Financial Risk Administrative Risk

YEJIN's Multi-Brain architecture allows multiple AI models to generate, compare, and cross-validate responses.

By combining:

RAG + Semantic Validation + Cross-Model Validation

YEJIN reduces dependency on a single AI model and adds an additional Trust Layer to conventional information security.

Data security protects what AI sees.

Multi-Brain helps strengthen what AI says.

PRIVATE AI

AI that operates inside your organization.

Organizations with high security requirements may require AI itself to remain within customer-controlled infrastructure.

YEJIN can deploy Private sLMs together with Local RAG and internal databases on customer-controlled GPU infrastructure.

Typical Private AI Flow

01User

02Internal AI Gateway

03Private sLM

04Local RAG

05Internal Database / Documents

06Validation Engine

07User

Prompts, organizational data, retrieval results, and AI-generated outputs can therefore remain within the customer's controlled environment.

Data Sovereignty

Your organization controls where its data is stored and processed.

AI Sovereignty

Reduce dependency on external AI platforms and maintain greater control over your AI environment.

Model Independence

Choose commercial LLMs, open-source models, and Private sLMs according to security, performance, and business requirements.

Closed Network AI

AI can be designed to operate in environments with limited or no Internet connectivity.

ZERO TRUST FOR AI

Never trust by default. Always verify.

YEJIN applies Zero Trust principles to AI architecture.

Users, applications, devices, AI agents, and connected systems should not automatically be trusted simply because they are located inside an organizational network.

Security policies should determine:

Who can use

Which AI to access

Which Data for

What Purpose with

What Level of Authority

Minimum privilege should apply not only to employees and applications, but also to AI Agents.

AI AGENT SECURITY

The more AI Agents can do, the more carefully they must be governed.

AI Agents are fundamentally different from conventional chatbots.

An AI Agent may:

Send an email
Generate a document
Query ERP data
Create an order
Access customer records
Update databases
Call external APIs
Trigger automated workflows

If an AI Agent receives excessive permissions, an AI error can become a real business action.

YEJIN designs AI Agent environments with controls such as:

Role-specific Agent permissions
Restricted tool access
Data access boundaries
Workflow-level authorization
Human approval for critical actions
Action logging
Agent-specific policies

AI Automation requires AI Governance.

SECURITY DEPLOYMENT

Security architecture aligned with your organization.

Deployment models, data processing, external AI and recommended organizations
Deployment Model	Data Processing	External AI	Recommended For
Closed On-Premise	Customer infrastructure	None	Government, public sector, finance, critical R&D
Private AI	Dedicated customer environment	None or highly restricted	Enterprises, manufacturers, research institutions
Hybrid AI	Internal + approved external services	Selective	Mid-size and large enterprises
Secure Cloud AI	Approved cloud infrastructure	Policy-based	SaaS, public services, general business workloads

YEJIN does not force every customer into the same architecture.

We evaluate:

Security Level Data Sensitivity AI Performance Cost Operational Requirements

and design the most appropriate deployment architecture.

WHY YEJIN

We do not simply deploy AI. We build a trusted AI environment.

01. AI and security are designed together.

AI models, RAG, enterprise data, networks, AI Agents, applications, and business systems are considered as one architecture.

02. Private AI can operate in closed environments.

On-Premise AI, Private sLM, and Local RAG enable deployment in high-security environments.

03. No single-model lock-in.

YEJIN's AI orchestration architecture can integrate commercial LLMs, open-source models, and Private sLMs according to customer requirements.

04. Data security and AI reliability are addressed together.

Multi-Brain cross-model validation provides an additional AI trust layer beyond conventional infrastructure security.

05. Designed for both B2B and B2G.

Enterprise data sovereignty and public-sector security requirements can be addressed through deployment architectures tailored to each environment.

SECURITY IMPLEMENTATION PROCESS

From security assessment to secure AI operations.

01 SECURITY ASSESSMENT

Analyze business processes, enterprise data, networks, systems, AI use cases, and security requirements.

02 SECURITY ARCHITECTURE

Design the appropriate:

Cloud · Hybrid · On-Premise · Closed Network

architecture.

03 PoC & VALIDATION

Validate AI performance, data connectivity, operational feasibility, and security within a controlled implementation scope.

04 SYSTEM INTEGRATION

Integrate:

RAG · Private sLM · AI Agents · ERP · CRM · Groupware · Databases · Enterprise Applications

according to approved access policies.

05 SECURITY HARDENING

Apply:

Access Controls · Network Policies · Data Controls · Logging · Monitoring · Operational Security Policies

06 OPERATION & GOVERNANCE

Establish:

Monitoring · Maintenance · Security Patching · Access Management · Auditability · AI Governance

for ongoing operations.

FAQ

Security Technology: Frequently Asked Questions

Yes. An On-Premise architecture using Private sLMs and Local RAG can be designed to operate without external Internet connectivity. The appropriate model size, GPU configuration, storage, and inference architecture are determined according to workload, concurrency, response-time requirements, and the customer's infrastructure.

In a fully closed On-Premise architecture, AI models and Local RAG can operate within the customer's infrastructure without sending internal organizational data to external AI APIs.

Yes, where permitted by the customer's security policy. YEJIN can design a Hybrid AI architecture that combines internal models or Private sLMs with approved external AI services.

Yes. APIs and data interfaces can be analyzed so that AI Agents only access the information and functions permitted by organizational policy.

They can, subject to applicable privacy and security requirements. Processing purpose, data scope, access rights, retention, deletion, masking, and other privacy controls should be reviewed before implementation.

Yes. Each AI Agent can be designed with separate: • Permissions • Data sources • Available tools • System access • Workflow authority Critical actions can also require human approval.

Yes. Private sLM, Local RAG, internal databases, and On-Premise inference infrastructure can be designed for networks with restricted or completely unavailable external connectivity.

YEJIN treats unreliable AI output as an AI trust and governance risk. RAG can be used to provide organizational evidence, while Multi-Brain cross-model validation compares multiple AI responses to reduce dependency on a single model.

YEJIN can design AI architectures to support applicable public-sector network, data, access-control, logging, and operational-security requirements. The exact requirements and compliance scope are reviewed according to the relevant institution, data classification, procurement requirements, and deployment environment.

SECURE AI. TRUSTED AI.

Protect your data. Govern your AI. Build trust into every decision.

AI adoption in government, public institutions, financial organizations, manufacturers, research institutions, and enterprises requires more than powerful models.

It requires control.

Control over:

Data Networks Models Users Agents Systems Decisions

YEJIN begins with one fundamental question:

Where should your data remain?

From there, we design who can access it, which AI may process it, what actions AI may perform, and how critical interactions can be governed.

Move beyond simply adopting AI. Build a Private AI environment you can trust.

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YEJIN Inc.

Human-Centered AI. Reliable by Design.

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Deployment Model

Data Processing

External AI

Recommended For

Closed On-Premise

Customer infrastructure

None

Government, public sector, finance, critical R&D

Private AI

Dedicated customer environment

None or highly restricted

Enterprises, manufacturers, research institutions

Hybrid AI

Internal + approved external services

Selective

Mid-size and large enterprises

Secure Cloud AI

Approved cloud infrastructure

Policy-based

SaaS, public services, general business workloads