Cybersecurity has a big data problem: the volume of security, threat and other contextual data that you have to process, analyze, store, and incorporate into your threat detection, investigation, and response processes has grown exponentially in the past few years.
With hundreds of new security incidents to deal with every week—each often-taking days to fully investigate—security analysts can quickly fall behind. They can miss important signals as they manually try to add context to the data by gathering information from multiple sources and tools.
Our data scientists and threat hunters continuously update the threat detection engine of Cysiv Command with new rules and use cases to ensure the best possible proactive protection from new threats.
Cysiv automates, accelerates, and improves the process of finding and prioritizing threats by rigorously applying a range of important data science techniques:
The Cysiv Command platform uses data science to more efficiently and effectively convert raw logs and data from a broad range of important sources into actionable, high-quality, high-confidence detections and security incidents that truly warrant deeper human investigation.
In a one-month period, Cysiv ingested over 56 billion security logs and events from a single customer, and distilled these into 221 actionable, high-quality, high-confidence security incidents that truly warranted deeper human investigation.
This highlights how effectively data science and automation are used to streamline and improve the threat detection, investigation and response process.
Our white paper provides a detailed description of Cysiv’s modern, data science-driven approach to more quickly, effectively and efficiently detecting and investigating cyber threats, and the security operations and analytics platform, which many would consider a next-gen security information and event management (SIEM), that enables it.
Ready to learn more? Get in touch with us today to discover how Cysiv can help your business identify and address cybersecurity vulnerabilities.