Scroll Top
Home » Gurucul Security Analytics and Operations Platform

Gurucul Security Analytics and Operations Platform

Real-Time Visibility & Detection, Prioritized Investigations, and Automated Response Across Entire SOC Lifecycle

Security Analytics Business Challenges

Security Teams are faced with numerous challenges when it comes to monitoring for, detecting, and responding to threats that have circumvented defensive measures.

Lack of visibility: Most SIEM and XDR solutions are limited in the data they can ingest to properly piece together puzzle pieces required to detect an attack campaign.

Overwhelming number of unprioritized alerts: Too many alerts make it impossible for security teams to identify the real threat and often have them chase false positives.

False claims of ML/AI combined with limited black-box machine learning models force manual correlation, prioritization, and investigation of seemingly random “puzzle pieces”, which lengthens Mean-Time-To-Detect (MTTD) by weeks or months.

No out-of-the-box detection and automation across the entire security operations lifecycle increases Mean-Time-To-Respond (MTTR).

Limited training for staff, too many tools to manage, and unpredictable capital and operational expenses putting them at a constant disadvantage against organized and persistent threat actors.

Cloud Native SAAS infographic

Gurucul Cloud-Native SaaS is Supported Worldwide.
View our Cloud Vendor Support and Regions.



  • Poly-Cloud Threat Detection, Investigation, and Response (TDIR)
    Deployment, security analytics, and detection on any cloud
  • Data Pipelines
    Automated Data Interpretation Engine to ingest data from any source automatically
  • Gurucul STUDIOTM
    Customizable security analytics and transparent machine learning models to accommodate custom use cases
  • Enterprise Risk Engine
    All-encompassing analytics-derived risk scoring to accelerate investigation and high-fidelity response
  • Threat Intel & Content
    Largest library of open threat models, MITRE ATT&CK mapping, & curated threat intelligence powered by Gurucul Threat Labs
  • Gurucul MinerTM
    Contextual search across all data silos


Gurucul has created a purpose-built Cloud-Native Security Analytics Platform and Operations Platform that goes beyond current XDR, SIEM and other SOC solutions to empower security analysts. With a consolidated set of capabilities, the platform helps to automate tasks beyond just collection and correlation and provides a full set of capabilities for threat detection, investigation, and response (TDIR). The platform is optimized to ingest as much data as possible, applying a wide area of analytics and using true ML/AI to adapt and learn to newer threats.

Gurucul Security Analytics and Operations Platform-How It Works


Drastically reduce overall operational expenses while improving the efficiency of TDIR programs across the board:

  • Support more data ingestion that is currently inhibited by licensing costs, heavy customization for new data sources, storage limitations, and poor analytics leading to too many alerts and false positives
  • Reduce threat detection time from weeks or months to minutes or hours through automation
  • Reduce manual effort through automation of tasks and prioritization of remediation actions with a rich level of context
  • Improve analyst efficiency and accelerate training through open and transparent models, gathered context, and clear response recommendations leading to improved TCO

Quickly identify and address new, emerging, and unknown threats that evade most signature-based solutions and rely on updates which could take weeks or months to develop.

Enable automated response, based on understanding overall risk, with targeted and dynamically created playbooks, which consider users, identity, network, and other context to accelerate remediation steps cross-functionally.

Reduce Capital Expenditures and Operating Expenditures with predictable, asset-based licensing.



A Truly Cloud-Native SOC Platform that Scales with Your Business
Eliminate trade offs between visibility and licensing costs by charging based on user/entity, not data ingestion.

The Most Comprehensive Analytics and Self-Learning ML/AI
Leverage out-of-the-box included Threat content, over 2500+ transparent and customizable ML Models, and widest-breadth of analytics.

Trusted and Transparent Automation Across Ingestion, Correlation, Detection, Prioritization, Investigation, and Response
Reduce prolonged manual efforts and deliver risk-driven context that lowers MTTD and MTTR from weeks or months to minutes and hours.

Gurucul Security Analytics Solution

Use Cases for Security Analytics and Operations

Supercharge Security Operations

Supercharge Security Operations

Consolidate and automate Security Operations to Improve MTTD and MTTR to minutes or hours

Prevent Successful Ransomware

Prevent Successful Ransomware

Maximum telemetry and the most comprehensive analytics to detect and respond to even new attacks and variants

Insider Risk and Threat Monitoring

Insider Risk and Threat Monitoring

Identify suspicious or malicious activity before it either becomes an external activity or inflicts damage.

Accelerate Zero Trust

Accelerate Zero Trust

With Identity security context, go beyond other solutions to understand current access, monitor for misuse and violations, and implement continuous improvements.



What is an example of security analytics?

An example of security analytics is the use of machine learning algorithms to analyze network traffic data in order to detect and prevent cybersecurity threats. This involves collecting and analyzing large volumes of data from various sources such as network logs, firewall logs, and intrusion detection system alerts.
Through the use of advanced data analytics techniques such as anomaly detection, clustering, and pattern recognition, security analysts can identify abnormal behavior and potential security breaches. This information can then be used to proactively respond to security threats and prevent them from causing harm to the organization’s systems and data.

Another example of security analytics is the analysis of user behavior to detect potential insider threats. By monitoring user activity, security analysts can identify unusual patterns of behavior that may indicate an employee is engaging in malicious activities such as data theft or sabotage. This can help organizations detect and prevent insider threats before they cause significant damage.

What is the difference between SIEM and security analytics?

SIEM (Security Information and Event Management) and security analytics are two related but distinct concepts in the field of cybersecurity.

SIEM is a centralized system that collects and analyzes security event data from various sources across an organization’s IT infrastructure. It provides real-time monitoring, threat detection, and incident response capabilities. SIEM systems typically include features such as log management, security event correlation, and automated alerting.

Security analytics, on the other hand, is a broader term that encompasses the use of various analytical techniques to detect and respond to security threats. It includes methods such as machine learning, data mining, and statistical analysis to identify patterns and anomalies in large datasets. Security analytics can be used for a wide range of applications, from network traffic analysis to user behavior monitoring.

In essence, SIEM is a specific type of security analytics tool that focuses on event management and correlation. While security analytics covers a broader range of techniques and applications beyond SIEM. So, while SIEM is a component of security analytics, security analytics is a broader category that encompasses many other tools and techniques beyond SIEM.

What are data analytics platforms?

Data analytics platforms are software tools that enable organizations to collect, process, analyze, and visualize large volumes of data from various sources. These platforms provide users with a range of analytical capabilities, including data visualization, statistical analysis, machine learning, and predictive modeling.

Data analytics platforms are essential tools for organizations looking to make data-driven decisions and gain insights from their data such as identifying trends, predicting outcomes, detecting anomalies, and optimizing performance. They are used across many industries, including finance, healthcare, retail, and manufacturing, among others.


Security Analytics Resources