ExeonTrace is a network detection and response solution that utilizes machine learning algorithms to monitor network traffic and identify cyber threats in real-time. It enhances organizational cybersecurity by providing immediate threat detection and response capabilities without the need for additional hardware sensors.
Funding
$5.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations face increasing challenges in detecting sophisticated cyber threats within their networks, especially as threat actors leverage techniques like encryption and supply chain attacks to evade traditional security measures. Existing security solutions often rely on data-heavy traffic mirroring or deep packet inspection, which struggle with encrypted traffic and high-bandwidth networks, leading to blind spots and delayed threat detection.
Solution
ExeonTrace is a Network Detection and Response (NDR) solution that uses machine learning algorithms to provide comprehensive network monitoring and real-time threat detection without requiring additional hardware sensors. By analyzing network metadata rather than relying on deep packet inspection, ExeonTrace overcomes the limitations of traditional security tools, offering visibility into encrypted traffic and high-volume network environments. The platform establishes a baseline of normal network behavior and identifies anomalies indicative of cyberattacks, including advanced persistent threats (APTs), ransomware, and supply chain compromises. ExeonTrace enhances an organization's cybersecurity posture by enabling rapid response to potential threats and providing insights into network vulnerabilities.
Target Audience
ExeonTrace is designed for organizations of all sizes seeking to enhance their cybersecurity posture, including CISOs, IT security teams, and managed security service providers (MSSPs).
Features
- Real-time network monitoring and anomaly detection using machine learning
- Analysis of network metadata, providing visibility into encrypted traffic without decryption
- Detection of algorithmically generated domains used in cyberattacks
- Behavioral analysis to identify deviations from established network baselines
- Integration with threat intelligence feeds for up-to-date threat identification
- Comprehensive visibility into IT and OT networks, including shadow IT
- AI-driven threat scoring for rapid investigation and response
- Integration with EDR solutions for a holistic view of network and endpoint activities