Autonomous Security
Autonomous Security Capabilities of Clumit Security
Clumit Security combines ClumL’s proprietary AI clustering engine, built with globally competitive technology, and security-optimized LLM integration to enhance both the accuracy and efficiency of security operations.
By reducing the resources consumed by repetitive tasks, it creates a new environment where security teams can focus on strategic decision-making and response, realizing the vision of true autonomous security.
Proprietary AI Clustering Engine
The core intelligence of autonomous security, capable of capturing even the flow of threats.
The AI Clustering Engine detects new and emerging intrusions in real time. By identifying multi-dimensional behavioral patterns in unstructured raw data, it uncovers attacks that legacy detection systems often miss.
Real-Time Comprehensive Traffic Monitoring
Monitors inbound, outbound, and lateral traffic to address multi-path threats such as lateral movement and data exfiltration.
Security-Optimized LLM Integration Technology
Working in tandem with generative LLMs, the system analyzes and reports detected threats in real time.
This gives administrators and SOC analysts an intuitive end-to-end view of threat activity, enabling faster judgment and more effective response.
자가 학습 기반 모델 최적화
네트워크 환경 변화에 따라 모델이 스스로 적응하며, 민감도와 탐지 정확도를 지속적으로 향상시켜 환경 변화에 자동 적응합니다.
ClumL’s Innovation: AI Clustering + LLM
LLMs alone cannot detect threats → ClumL integrates LLMs only with validated detection results from AI clustering.
Attempting detection with LLMs alone leads to unsolvable challenges: excessive cost and resource consumption, physical limitations, and accuracy issues.
Cyber Threats Solved by Autonomous Security
The Need for Autonomous Security
Autonomous Security minimizes human intervention and allows systems themselves to predict, detect, analyze, and respond to cyber threats autonomously.
It shifts the security paradigm from “after-the-fact response” to “proactive prevention” and “adaptive resilience.”
Known threats only
Signature-based detection is vulnerable to unknown threats such as zero-day attacks.
Slow response speed
Automated attacks can compromise systems in seconds or minutes, far outpacing human responses.
Limited learning ability
Predefined rules and playbooks cannot adapt to new attack techniques.
Alert fatigue
Tens of thousands of alerts per day overwhelm small teams of analysts.
A Paradigm Shift
in Security
Intelligent Threat Detection
Detects unknown and emerging threats with AI/ML technologies
Real-Time Response Capability
Automates detection through to response, drastically reducing response time
Continuous Learning & Adaptation
Learns new attack patterns, enabling defense systems to evolve autonomously
Reducing Workforce Burden
Addresses the global shortage of ~4 million cybersecurity professionals with an efficient alternative
Comparison: Autonomous Security vs. Traditional Approaches
자율주행이 AI가 대부분 행동하고 사람이 최소한으로 개입하는 것처럼, 자율보안도 AI가 실시간으로 위협을 관리하고 전문가의 판단은 필요한 경우에 더해집니다.
Human Role
Traditional Security
Automated Security
