AI Security Analytics Platform | Intelligent Security Platform eyeCloudAI

What is AI Agent?

Agent AI goes beyond simply answering user questions — it is a proactive artificial intelligence that autonomously formulates plans and uses tools to achieve assigned objectives. While traditional AI focuses on "question → answer," Agent AI performs multi-step actions to solve problems.

Autonomous Decision-Making: Makes independent threat determinations based on security policies and data.
Tool Utilization: Integrates with existing security tools such as SIEM and SOAR to directly execute actions like blocking and quarantine.
Continuous Learning: Continuously optimizes detection models through self-feedback loops to adapt to evolving attack techniques.

Core Objectives of AI

Completion of Autonomy and Collaboration

Self-judging, self-executing autonomous security: minimal human intervention through AI agents' unmanned detection·response and autonomous decision-making
Intelligent ecosystem that evolves through collaboration: multi-agent based solution orchestration and self-optimization of detection models through learning

Operational Innovation and Expertise

Zero repetitive tasks, focus on advanced strategy: maximize operational efficiency through automatic processing of hundreds of millions of logs and AI assistant summaries
Proactive defense against unknown threats: detect zero-day threats through UEBA integration and autonomously track·analyze complex APT attacks

Autonomous Driving of Security

Full autonomous operations (SOC Autonomy): uninterrupted security operations through policy-based unmanned response and automatic playbook execution
Continuously evolving self-evolution model: infrastructure that self-evolves to match changing attack techniques through self-feedback loops

Benefits

The Future of Human-Centered Autonomous Security Operations

Agentic AI(Goal)

Autonomous SOC

LLM-based multi-agent (collection/analysis/response) Full SOAR automation with autonomous response

AI Security(Advanced)

AI model-based anomaly detection

Playbook-based automated response (Human-in-Loop)

AI Assistant(Current)

Support for security analysts

Threat analysis assistance and RAG-based knowledge answers

Enhanced Security Operations Efficiency

Reduced security operations time (MTTD, MTTR) through AI automation
Lower operating costs through streamlined threat response processes

Improved Threat Response Accuracy & Reliability

Minimized false positives by combining AI analysis with human strategic judgment
Trustworthy threat response framework tailored to each organization

Sustainable Autonomous SOC Virtuous Cycle

Flexible response to organizational/environmental changes via human-centered automation
Virtuous autonomous SOC structure through continuous AI model learning

Reduced Threat Analysis Time

Analysis time that previously took at least 10 minutes is now processed in seconds by AI.

Manual analysis —
initial assessment takes 10 min ~ hours

Automated — under 10 sec
60x+ staffing capacity effect

Conversational AI Assistant specialized in Cybersecurity

LLM-based conversational assistant 'eyeCloudAI Assistant' trained on Korean security expertise.

LLM-based conversational assistant trained on security expertise

Information Security Advisor specialized in Cybersecurity

Natural language conversational Q&A

Attack type mapping with external knowledge bases (CWE, CVE)

Detection result and threat info summary, automated report generation

Conversational Q&A

"Show me users with the most failed logins last week"

"Hong Gildong, user 123, admin..."

Find what you need naturally without expert knowledge

Diverse Expression Recognition

"Anything meaningful about this IP?" "Analyze this payload" "Payload analysis request!"

"Analysis result: attack attempt detected..."

Understands and responds to varied query expressions

Faster Attack Response

"Explain CAPEC-96 attack technique!"

"This technique... (brief explanation)"

AI extracts needed info from SIEM data instantly

Strengths

Next-generation AIP (Artificial Intelligence Platform)

Container Orchestration based large-scale infrastructure auto-operation platform

Container Orchestration based large-scale infrastructure auto-operation platform
Runs on a powerful dedicated infrastructure integrating AIOps, Data Lake, and Container Orchestration

Solution

Autonomous AI

AI Analytics

AI Assistant

AI Agent

Agentic AI

Platform

Aip(Artificial Intelligence Platform)

AIOps

Meta Store

Data Lake

Container Orchestration

Solution layer : end-user products
Platform layer : in-house dev/operations infrastructure (AIP), customer-built AIP for enterprise clients

Verified Accuracy & Efficiency at Korea's Largest Security Operations Centers

99.8% Detection Rate & Accuracy

Boasts high detection rates and accuracy by applying AI models that have been repeatedly trained and reinforced with over 10 years of accumulated attack data from security operations centers and various new attack data from our white-hat team.

15x Server Deployment Effect

Through AI model virtualization and parallel distributed processing technology, 15 virtualized PODs run simultaneously, delivering the equivalent of 15 H/W deployments from a single unit.

AutoML Maximizes Modeling Convenience

Easier and More Accurate AutoML Modeling

AutoML reduces AI modeling steps from 9 to 3
AI recommends the optimal model (the fastest and most accurate), making it easy to choose the right model for your purpose.
Hyperparameter optimization is applied with just a click, recommending high-accuracy hyperparameters automatically — enabling high-accuracy model creation regardless of user experience.

Solution layer : end-user products
Platform layer : in-house dev/operations infrastructure (AIP), customer-built AIP for enterprise clients

Efficient Security Operations Support

Information Security Advisor That Understands You Best

An assistant that operates in a safe on-premises environment with no data leakage concerns. (Also operates in air-gapped environments) It particularly supports specialized AI assistant tasks for SOC operations (payload analysis, specific knowledge explanation, etc.). Through customized learning that reflects the environment and operational characteristics of specific enterprises, it provides answers optimized for the organization.

Integration with eyeCloudXOAR Playbook & Automated Analysis Processing

Functions like data feedback and additional (continuous) model training are performed in eyeCloudXOAR. In addition, integration with Playbook enables automated processing based on AI analysis results.

Key Features

Lifecycle Management Functions for Sustainable Models

Performs real-time risk analysis and detection by generating anomaly detection and true/false-positive analysis models based on machine learning

Performance Dashboard

Metric setup for automation performance measurement
Metric setup for model performance monitoring

Reinforcement Learning

Improve detection rate and analysis accuracy via reinforcement learning

Analysis-based Response & Management

Security response/action by personnel based on analysis/detection results
Reinforcement learning utilization with new threat data
New model creation and management

Data Collection, AI Model Generation

Threat/normal data collection
Generate anomaly detection and true/false-positive analysis models via machine learning

Model Generation & Training

Machine learning for threat analysis/detection
Update data and training methods based on results

Threat/Analysis Detection

Real-time analysis/detection by AI models
Anomaly detection (unknown threats)
True/false-positive analysis and classification

MonitoringUsers can review accumulated log results through date range settings(Real-time true/false-positive ratio monitoring)

Monitored results identify real-time detection retraining timings and provide an optimal security environment

Analysis count : total log count within the current search period
Analysis distribution : distribution of searched logs by AI prediction
Accuracy distribution by result : distribution within each accuracy range by AI prediction
Top attack names : TOP 5 most frequent attack names
Analysis automation rate : ratio of logs that operators don't need to analyze separately, based on the current detection model usage compared to total daily logs (1-day prior)
Model accuracy : ratio of logs that don't require feedback among total daily logs (1-day prior)
Analysis automation rate & model accuracy trend : trend graph of analysis automation rate and model accuracy

MonitoringDetailed history of detected processes available

Within the monitoring screen, detection model info and training history are easy to view, and detailed history of each detected process can be checked

Detection Model Information

Detection info : detection name, ID, filter query, model & version, reinforcement learning count
Detection Pod info : pod name, status, image used, creation time, Log

Training History Inquiry

Training history : view training history of models used in detection
Data : view data used for training
Evaluation : view training evaluation
Log : view training Log generated during training

Provides correction for false-positive features through feedback and reinforcement learning

Log inquiry, filtering, and search per detection model are available

Log inquiry : view logs generated within the current search period
Filtering : filter by AI prediction, accuracy range, feedback status
Search : field=keyword search within currently searched logs, with detail search providing in-result re-search and exclude functions
Feedback & reinforcement learning : add feedback Labels to searched logs, save them, then manage information and request reinforcement learning

Original log feedback screen Sub image

Feedback list screen Sub image

Integration with eyeCloudXOAR for AI Model Detection Log Inquiry and Model Training Status Verification

Performs reinforcement learning through user feedback to improve detection accuracy

XOAR Feedback/Reinforcement Learning Management Screen

Log inquiry : view logs detected by AI models within the input period
Feedback : directly check logs in XOAR and add Feedback Labels to save
Reinforcement learning : request reinforcement learning with saved feedback data

XOAR Feedback/Reinforcement Learning Management - Training History Screen

Model training history : check the past training history and current training status of the selected model
Training data details : view the list of data used for training

Contact Us

Business protected by AI and data Experience the optimal solution that stands firm against evolving threats. Please provide the necessary information for smooth product consultation. SecuLayer's dedicated specialist will contact you promptly.

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AI Analysis & Operation Solution (AI&AI Assistant) - eyeCloudAIIntegrated Security Analysis Solution (SIEM) - eyeCloudXOAR SIEMBehavior Analytics Solution (UEBA) - eyeCloudXOAR UEBAIntegrated Security Response Solution (SOAR) - eyeCloudXOAR SOAROther

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