Muninn provides AI-powered Network Detection and Response (NDR) technology that continuously monitors network traffic to detect and respond to cyber threats in real-time. By minimizing false positives and ensuring 100% threat detection, Muninn enables organizations to protect their critical digital assets from sophisticated cybercriminals.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations face an increasing volume of sophisticated cyber threats that bypass traditional security measures like firewalls and antivirus software. Security teams are often overwhelmed by the sheer number of alerts, leading to delayed response times and potential breaches. The need for continuous, real-time network monitoring and threat response is critical but resource-intensive.
Solution
Muninn provides an AI-powered Network Detection and Response (NDR) solution that continuously monitors network traffic to detect and respond to cyber threats in real-time. By leveraging unsupervised machine learning, Muninn establishes a baseline of normal network behavior and identifies anomalous activities indicative of cyberattacks and insider threats. The platform minimizes false positives, allowing security teams to focus on genuine threats and respond more effectively. Muninn offers both threat detection and automated response capabilities, ensuring comprehensive network protection.
Target Audience
The primary target audience includes organizations of all sizes across various industries seeking to enhance their cybersecurity posture with AI-driven network detection and response capabilities, with a focus on security operations centers (SOC) and IT security teams.
Features
- AI-powered threat detection using unsupervised machine learning to identify unknown attacks and insider threats
- Real-time network traffic analysis with detailed insights and metadata extraction
- Automated response to cyber threats, enabling rapid containment and remediation
- Chain of Events feature connecting seemingly normal events over time to reveal evolving attack chains
- Baselining of normal network behavior to accurately identify anomalies
- Low false positive rate, reducing alert fatigue and improving security team efficiency
- EU-based infrastructure
- On-prem/offline mode