ESG Technical Review of the Gurucul Security Analytics and Operations Platform

by Jane Grafton on July 28, 2022

ESG Technical Review of the Gurucul Platform

Enterprise Strategy Group (ESG) evaluated the Gurucul Security Analytics and Operations Platform to validate the tangible benefits of automated detection and response of internal and external threats within on-premises and cloud environments. Areas of focus included deployment flexibility, data ingestion capabilities, and machine learning (ML) risk analysis. ESG focused on how these capabilities can help organizations secure their infrastructure confidently, regardless of the experience level of their security operations center (SOC) team.

SOC Challenges

48% of enterprise organizations’ SOC teams are impacted by the skills shortage, continually challenged with increased workloads, complex threat landscape and meeting operational targets for digital transformation. Many companies must fill SOC roles with less experienced professionals. Analysts with experience are often overwhelmed with thousands of events and anomalies every day, along with noise caused by false positive alerts, making it difficult to know what threats require immediate action.

Organizations need a SOC platform capable of helping inexperienced analysts make a difference from day one while providing the flexibility to expand SOC team capabilities over time.

What Functionality Did ESG Evaluate?

ESG examined how the Gurucul Security Analytics and Operations Platform can empower the SOC team to quickly detect and remediate security threats with confidence and minimal business disruption. ESG validated a SOC team scenario outlining how Gurucul enables SOC teams in five essential ways:

Data ingestion
Machine learning and analytics
Risk scoring
Prioritized alerts, investigations, and reporting
Automated playbooks

What Did ESG’s Testing and Analysis Reveal?

ESG verified the ease of using the automated data ingestion capabilities that learn how to extract the right security metadata continuously, requiring no or little customization by administrators
The Gurucul platform’s risk analysis clearly ascertains when suspicious activity and behaviors can be confidently and accurately identified as malicious so analysts can take appropriate action
SOC analysts are given the power to respond quickly to the highest-priority threats in the overall attack campaign
The ML models are designed to allow SOC analysts to remediate with the highest precision and the smallest possible disruption to the business

Why This Matters

Skilled cybersecurity resources are limited in organizations of all sizes, while the volume and severity of threats continue to grow, and threats occur more frequently. The combination of inexperienced analysts and noisy tools adds to the stress on SOC teams and increases the risk to organizations.

Sponsorships Available

ESG confirmed that all companies, from large, mature enterprises to small businesses, struggle with teaching their SOC team how to hunt for threats and can benefit from the unique capabilities found in the Gurucul Security Analytics and Operations Platform. ESG walked through the Gurucul platform’s powerful machine learning models, straightforward integration points, and automated response and validated how they can help SOC teams detect and remediate threats with confidence and limited business disruption.

While this report does not focus on service providers, it’s important to note that managed detection and response (MDR) providers are not immune to the challenges described here and could benefit from the Gurucul platform.

The bottom line? If your organization needs advanced threat detection and response capabilities while making threat hunting and analysis practical for analysts of any skill level, ESG recommends you should seriously consider Gurucul Security Analytics and Operations Platform.

The Gurucul Security Analytics and Operations Platform

The Gurucul Platform is designed to enable organizations to react to threats quickly using advanced data analytics and machine learning models. Unlike rule-based security solutions that essentially use fixed flowcharts, Gurucul’s platform uses over 2,500 transparent and customizable ML models to learn on the fly, identify new and emerging threats, and react to attacks in real time.

The platform is deployable into any environment and contextually links data coming from multiple sources to reduce noise and enable SOC teams to focus on investigating full attack campaigns. Unlike traditional SOC platforms, Gurucul does not price their offering based on the amount of data ingested, and instead prices per user or entity, so as the volume of data increases, licensing costs remain predictable.