NewEvol is a data security platform that utilizes machine learning algorithms and a centralized console to provide real-time threat detection, automated incident response, and comprehensive data monitoring across IT environments. The platform addresses the challenge of managing multiple security tools by integrating data ingestion, analytics, and orchestration into a single solution, significantly reducing false positives and operational inefficiencies.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations face challenges in managing the increasing volume and sophistication of cyber threats, often relying on multiple, disparate security tools that create operational inefficiencies and generate numerous false positives. This complexity strains security operations teams, increases response times, and leaves gaps in threat detection.
Solution
NewEvol offers a dynamic threat defense platform that consolidates data ingestion, advanced analytics, and automated orchestration into a single console. The platform leverages machine learning algorithms and threat intelligence feeds to provide real-time threat detection, reduce false positives, and automate incident response. By integrating SIEM, data lake, SOAR, and threat intelligence capabilities, NewEvol streamlines security operations, enabling organizations to identify and respond to threats more efficiently. The platform's unique 2D and 3D algorithms help uncover unknown threats that may go unnoticed by traditional rule-based systems.
Target Audience
The primary target audience includes security operations centers (SOCs), IT departments, and cybersecurity professionals seeking to streamline threat detection, automate incident response, and improve overall security posture.
Features
- Centralized console for managing security operations, including log search, remediation, and evidence collection
- Automated playbooks for monitoring, detection, and response processes, enabling autonomous actions
- Dynamic threat analytics using 2D and 3D algorithms to detect abnormalities in data
- Integrated Decision Support System (DSS) with SOAR to reduce reliance on security analysts
- Real-time threat intelligence feeds from global sources
- Machine Learning (ML) algorithms to detect abnormalities in data
- Data Lake for storing and analyzing large volumes of data
- SIEM for real-time security monitoring and advanced threat detection
- Orchestration and Response capabilities for automated incident response