AI/ML-driven, petabyte-scale, real-time network defense and threat intelligence, with source code licensing available.
Full source code availability and machine learning models are subject to compliance and special licensing agreements.
Cyberattacks are not just technical incidents; they are devastating financial blows and severe reputational risks for organizations globally. Understanding the tangible impact of these threats underscores the critical need for advanced cybersecurity.
Paralyzing operations and extorting millions, ransomware attacks disrupt businesses globally, leading to significant financial and reputational damage.
Denial-of-Service attacks cripple online services, resulting in massive revenue loss and customer dissatisfaction due to prolonged downtime.
Exploiting vulnerabilities to steal data or compromise systems, injection attacks lead to severe data breaches and regulatory fines.
Often a precursor to larger attacks, persistent scanning probes for weaknesses, exposing organizations to future exploitation.
Compromising user sessions and delivering malicious content, XSS attacks undermine trust and can lead to widespread data theft.
Providing covert access to systems, backdoors enable persistent espionage and long-term data exfiltration, often undetected for extended periods.
In today's complex digital landscape, traditional security measures are no longer sufficient. CyberNeuro-RT was built to give organisations proactive and intelligent threat intelligence, using machine learning to provide visibility and automated threat detection across the network rather than at its perimeter.
It came out of a Department of Energy SBIR Phase II programme on HPC-scale cyber-threat detection, developed by Quantum Ventura in partnership with Lockheed Martin's MFC Division and Pennsylvania State University. The multi-tenant platform is built for scale and requires only a lightweight network listener on the client side, so it deploys without re-architecting the network it protects.
How CyberNeuro-RT detects, what it trains on, where it runs, and how it presents what it finds.
75x dataset growth in under 2 months.
Two offerings from the leading neuromorphic developers: Intel and Brainchip.
A robust, multi-faceted, user-friendly cyber analyst dashboard prevents operator fatigue that allows cyber attacks to happen. Large numbers of false alarms cause real threats to be missed, and false alarms fatigue the cyber analyst, further increasing the risk of missed threats.
Leverage our AI model ensemble evaluated with the ML-X platform for superior threat detection.
Benefit from immediate malware detection and alerting, both online and offline.
Conduct full-text searches for comprehensive forensic investigation and threat hunting.
Utilize autonomous AI agents integrated with real-time analytics and scalable data lakes.
Deploy and evaluate top-performing security models live to optimize detection.
Gain deeper insights with customizable, interactive dashboards and comprehensive reporting for better security posture analysis.
Automated AI scripts for endpoint isolation and deployment of dynamic honeypots.
Comprehensive analysis of network traffic aggregated by time and specific endpoints.
Built on hybrid cloud and Kubernetes for unlimited, scalable storage and performance.
Our multi-tenant SaaS architecture requires only a lightweight network listener on the client-side. Clients gain access to a private dashboard for seamless monitoring and selection of specific network interfaces.
Access round-the-clock premium technical support from our cybersecurity experts to ensure continuous protection.
For organizations with specific compliance or infrastructure needs, we offer flexible on-premise, cloud and hybrid deployment options.
A Quantum Ventura, Lockheed Martin, and Penn State Innovation. CyberNeuro-RT (CNRT) has been developed in partnership with Lockheed Martin Co.'s MFC Division and Pennsylvania State University under partial funding from the U.S. Department of Energy.
Neuromorphic Computing Lab · Collaborating partner
Funded by
Funded by
MFC Division · Collaborating partner
| Partners | Lockheed Martin Co. MFC Division · Pennsylvania State University |
|---|---|
| Funding | Partial funding from the U.S. Department of Energy |
| Deployment | CPU, GPU or low-power neuromorphic chip |
| Neuromorphic | Intel Loihi · Brainchip Akida |
| Architecture | Multi-tenant SaaS, hybrid cloud and Kubernetes |
| Licensing | Source code licensing available, subject to compliance agreements |
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Quantum Ventura