Business | Network Integration (NI)

Network Integration (NI)

AI-driven real-time fault
monitoring automation tools

AS-IS

Manual infrastructure care

Operations rely on people, focusing on network-centric systems

monitored through NMS while network staff

manually operate and respond to faults

which limits their response capabilities.

Communication

Operations

target systems

add

NMS

Limited

fault-focused management

arrow_forward

Cooperation

People-led

fault response

TO-BE

Human + automation tooling

Combining people with automated tasking tools lets us centrally manage operational systems, syncing network analytics, NMS, and hypervisors

(Apstra, NSX) via APIs and leveraging IBA monitoring to deliver next-gen unified automation.

Leading AI-driven shifts

in network operations management

Automating the entire fabric lifecycle from design to operations

Validated Designs

Build Automation

Deployment Automation

Operations Automation

• Network topology templates

• Rack layout, quantities,

and physical cabling,

and server structure definitions

• Enter real-world information

into topology templates

• Apply settings for your system

• Enter device information

• ZTP auto-configuration

• VM-based networking

• Intent-based rendering

Fabric auto-configuration

• Detect anomalous behavior

and notify user after analysis

• Zero-downtime expansion

Auto network configuration

Don't hesitate to adopt AI

and lead technological innovation

The vision AI offers is compelling, but it also carries clear risks. That's where AI governance comes in as a necessary safety net. AI is vulnerable to human bias and error, so a structured, systematic approach is needed to mitigate potential risks. AI governance provides this by establishing processes, standards, and safeguards to ensure AI activities align with ethical standards and societal expectations. A well-designed AI governance oversight process prevents and addresses risks like bias, privacy violations, and misuse, while fostering both innovation and trust.

Whynet pursues responsible AI that goes beyond legal compliance to include social responsibility. We will protect customers and technology from financial, legal, and reputational risks, and continue to support trustworthy growth.

AI's purpose is to

enhance human capabilities.

Data and insights are

the customer's rights and assets.

AI systems must be transparent

and explainable.

A framework for

responsible AI governance

Task

Ensure Stable Operations

Manage Risk & Build Trust

Strengthen Regulatory Compliance

Meet Stakeholder Needs

Planning

(Planning)

Define key metrics to measure

AI adoption across the organization.

Review existing processes for

AI fairness and explainability checks.

Analyze gaps between current

and potential AI regulations.

Review responsible AI competencies

and align with business goals.

Build

(Build)

Ensure traceability and

auditability of existing processes.

Operate updated processes and checks

throughout the AI lifecycle.

Create and ensure

accessibility of model documentation.

Assign roles, capabilities, and learning

tasks for responsible AI implementation.

Operate & Generate

(Operate & Generate)

Establish automated documentation

and management for model metadata.

Continuously validate AI fairness,

explainability, quality, and governance.

Support an environment for

burden-free compliance.

Build scalable and sustainable

workflows with stakeholder approval.

Risk Management

Differentiated compliance items by risk level

* Mandatory Compliance

* Follow existing procedures

High Risk

Ethical Principles Review

Supervisory Controls

Training Data

Bias

Data Protection

Explainability

Fairness

Performance

Customer Protection

Security

Medium Risk

Ethical Principles Review

Supervisory Controls

Training Data

Bias

Data Protection

Explainability

Fairness

Performance

Customer Protection

Security

AI Governance Framework

Strategy

add

Risk

add

Process

add

Oversight

add

Monitoring

Based on the EU AI Act's risk management framework, we apply differentiated compliance requirements

and reflect domestic financial authorities' laws and regulations to protect customers from AI risks.

Compliance

When designing and operating AI services and systems, it is necessary to analyze AI-related laws, institutions, and regulatory frameworks applicable domestically and internationally, and to reflect the specific compliance matters and standards required by law. It is also necessary to establish and consistently apply guidelines for each stage of planning, development, and operation by referring to domestic and international standards for key AI attributes such as fairness, performance, and explainability.

Phased System Establishment

To ensure that networks and IT infrastructure are stably managed throughout the entire lifecycle of planning, building, operating, and improving, it is necessary to apply phased AI governance based on the lifecycle, and to provide services that satisfy stability, scalability, and security by operating a standardized governance system throughout the entire process of real-time monitoring, log collection/analysis, and update and replacement management.