In our newest whitepaper, “Why Traditional Cybersecurity Tools Cannot Defend Against Zero-Day and No Signature Attacks,” we dive into how traditional cybersecurity tools work, why this fundamentally limits them from being able to detect zero-day or previously unknown attacks, why the industry standard for breach detection is around six to eight months and how modern, contextually-aware AI overcomes the limitations of traditional cybersecurity solutions.
Despite its inherent flaws, today’s SIEM software solutions still shine when it comes to searching and investigating log data. One effective, comprehensive approach to network security pairs the best parts of SIEM with modern, AI-driven predictive analysis tools. Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.
MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.
Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.
COVID-19 has caused most corporate businesses that remain open to shift to a work from home, remote workplace. Because of this, the cybersecurity industry has been turned on its head. Security teams went from monitoring and protecting established network environments to quickly pivoting their tools, resources, and oversight to manage a distributed workforce. This has …