Gurucul Next-Gen SIEM Nabs Top SIEM in Cyber Defense Magazine’s 11th Annual InfoSec Awards
Gurucul, the leader in Next-Gen SIEM, UEBA, XDR and Identity & Access Analytics, today announced that Gurucul Next-Gen SIEM has won the Top InfoSec Innovator Award for Cutting Edge Security Information and Event Management (SIEM) from Cyber Defense Magazine (CDM), the industry’s leading electronic information security magazine. Gurucul Next-Gen SIEM has once again been recognized for allowing users to discern context by cross-validating identity access, user behavioral and business application data to precisely identify real threats before exfiltration occurs.
“Today’s SOC teams must monitor, detect and respond to a growing number of security threats as the threat landscape continues to evolve,” said Saryu Nayyar, CEO of Gurucul. “Modern artificial intelligence and machine learning allow us streamline processes that typically overwhelm SOC teams with false positives and unprioritized alerts. Gurucul Next-Gen SIEM uses over 2500 Machine Learning Models to produce actionable risk intelligence that drastically reduces operational expenses while improving the efficiency of Threat Detection, Investigation and Response (TDIR) programs. We’re humbled to be recognized for this innovation and named amongst an incredible lineup of winners for the Top InfoSec Awards.”
“Gurucul embodies three major features we judges look for with the potential to become winners: understanding tomorrow’s threats, today, providing a cost-effective solution and innovating in unexpected ways that can help mitigate cyber risk and get one step ahead of the next breach,” said Gary S. Miliefsky, Publisher of Cyber Defense Magazine.
Gurucul Next-Gen SIEM offers a cloud-native, unified, and modular platform for consolidating core SOC solutions into a single pane of glass aligned with the evolving needs of the modern enterprise threat landscape. It leverages over 2500 Machine Learning Models powered by data science to produce actionable risk intelligence and can quickly identify and address new, emerging, and unknown threats that evade rule-based ML solutions. This drastically reduces overall operational expenses while improving the efficiency of Threat Detection, Investigation and Response (TDIR) programs. That includes supporting more data ingestion, heavy customization for new data sources, the reduction of threat detection time from weeks or months to minutes or hours, the automation of tasks and prioritization for remediation actions, and more.
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