ReddRadar
What is NewEvol?
NewEvol provides a comprehensive, all-in-one cybersecurity platform designed as a dynamic threat defense system to adapt to evolving security needs. It consolidates multiple security functions into a single console, simplifying security operations management by allowing users to search, hunt threats, and automate tasks efficiently. The platform is built to ingest diverse data types, including logs, events, alerts, and communication, facilitating thorough analysis and real-time threat identification.
Leveraging advanced technologies like machine learning, NewEvol focuses on enhancing threat analysis, detection, and response capabilities. It aims to improve operational efficacy by identifying threats in real-time, enabling quick responses to security incidents, enforcing configurations, and analyzing risk through identity and context awareness. The system incorporates features like automated playbooks and a decision support system to reduce false positives and dependency on manual intervention for routine tasks, ultimately providing centralized visibility and proactive protection against cyber threats and vulnerabilities.
Features
- Data Lake: Store and analyze petabytes of data for comprehensive insights.
- SIEM: Perform real-time security monitoring and advanced threat detection.
- Analytics: Utilize predictive analytics based on threat hunting and machine learning.
- Orchestration and Response (SOAR): Automate threat response actions and manage incidents efficiently.
- Threat Intelligence: Integrate updated threat feeds from various sources for proactive defense.
- Single Console Management: Manage all security operations, including search, hunting, and automation, from one interface.
- Machine Learning Algorithms: Employ unique ML algorithms (including 2D/3D techniques) to detect known and unknown abnormalities.
- Decision Support System (DSS): Automate routine tasks and decisions to reduce reliance on L1 support.
- Flexible Data Ingestion: Ingest any form of data including logs, events, alerts, and communication.
Use Cases
- Consolidating multiple security tools into a single platform.
- Automating security incident detection, investigation, and response.
- Improving security operations efficiency and reducing analyst workload.
- Detecting advanced and unknown threats using machine learning.
- Managing and analyzing large volumes of security data.
- Enhancing threat hunting capabilities with predictive analytics.
- Reducing false positive alerts through automation and analytics.
- Integrating threat intelligence for proactive security measures.
