By integrating our proprietary deep learning models, and Large Language Models, Turium Zebra goes from a reactive to a predictive approach to protect against both known and never-seen-before threats using natural language. Our Mixture of Experts Model has consistently outperformed other machine learning models for malware detection with low false positives.​

While there are millions of pieces of malware in existence, and thousands of software vulnerabilities waiting to be exploited, there are only handful of exploit techniques attackers rely on as part of the attack chain – and by taking away the key tools hackers love to use, Turium Zebra stops zero-day attacks before they can get started.​

Turium Zebra for Linux identifies sophisticated attacks as they happen without requiring a kernel module, orchestration, baselining, or system scans. Avoiding costly downtime, overloaded hosts, or stability snafus caused by traditional security tools with a single agent with optimised resource limits (including CPU, memory, and data collection limits). Behavioral and exploit runtime detections identify threats including container escapes, kernel exploits, and privilege escalation.​

Fault tolerance is managed through redundant systems and automatic failover mechanisms that are designed to ensure continuous operation even when individual components fail. Network redundancy, coupled with error-handling and self-healing capabilities, ensures that data transmission and processing continue without interruption. This means data is buffered even during partial and full system outages, during critical periods.​