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Next Generation SIEM

The integration of Big Data with Security Information and Event Management (SIEM) services has revolutionized the landscape of cybersecurity. Leveraging the capabilities of big data analytics, SIEM systems provide real-time monitoring and threat detection for enhanced detection of potential threats and anomalies across diverse data streams.

Currently, we are in the midst of another revolution in the field, transitioning to what is called Next Generation SIEM. These SIEM systems integrate advanced capabilities of Machine Learning and AI in a structured manner.
At WeAnkor, we follow an approach that continuously adopts new technologies.

The company’s development team integrates SIEM systems, Automation systems, and AI systems in order to achieve the optimal implementation of Next Generation SIEM (NG SIEM).

Elevate Your Business With Us!

We at WeAnkor offer NG SIEM solutions based on several alternatives:

  • The first is the implementation of the CrowdStrike Next Gen SIEM system, which includes all the relevant capabilities for this domain as part of its platform.
  • The second is the implementation and development based on Elastic SIEM, which includes advanced Big Data capabilities, Machine Learning features, advanced mechanisms for rule creation, automation for incident handling, and more.
  • The third alternative is the use of We ANkor’s MSSP system, a highly advanced and developed platform that combines various technologies, including the integration of multiple SIEM systems, Hyper Automation systems, and AI engines that enable rapid investigations and efficient conclusions, allowing for quick and effective incident response.

 

 

WeAnkor’s Big Data SIEM solutions utilize scalable frameworks such as ElasticSearch and LogScale to efficiently handle vast amounts of data related to security events. With centralized monitoring and analysis of security events across both cloud-based and on-premises environments, we deliver improved threat detection, compliance management, and reduced response times to improve an organization’s security posture.

Key Features

Real-Time Monitoring
and Analysis

Establishes baselines of normal behavior for users, systems,
and network entities, flagging deviations as potential security risks.

Correlation and
Contextualization

Analyzes diverse data sources for a holistic view of the security landscape, establishing relationships between seemingly unrelated events

Long-term
Data Retention

Offers extended storage capacity for security related data, enabling analysis of historical trends and proactive threat mitigation.

Customizable Dashboards
and Reporting

Provides tailored visualizations and reports to gain
actionable insights from big data analytics.

Continuous
Risk Assessment

Evaluate user risk levels in real-time,
considering factors such as location, device, and behavior patterns.

FAQs

What are the “5 V's” of Big Data?

  1. Volume: the massive amount of data generated and stored
  2. Velocity: the speed at which data is produced and processed in real-time
  3. Variety: the diverse types of data, including structured, semi-structured, and unstructured
  4. Veracity: the reliability and quality of the data
  5. Value: the insights and benefits derived from analyzing the data

What are some common challenges faced in Big Data analysis?

Common challenges include: managing data volume, velocity, and variety; ensuring data quality and veracity; addressing security concerns; handling real-time processing; and dealing with the complexities of distributed computing environments.

What is the role of machine learning in Big Data?

Machine learning plays a crucial role in Big Data by enabling predictive analytics, pattern recognition, and automated decision-making from large and complex datasets. It automates the process of identifying trends and insights that would be impossible to detect manually, and helps in implementing AI algorithms for advanced analytics.

How does a Big Data SIEM solution work?

A Security Information and Event Management (SIEM) solution collects, analyzes, and correlates security event data from multiple sources to detect threats, generate alerts, and support compliance efforts. It enables comprehensive monitoring and real-time responses to cybersecurity events.

What are the benefits of a Big Data SIEM solution?

A Big Data SIEM leverages modern data processing frameworks, including machine learning and AI, to handle large volumes of logs and security events efficiently, providing faster threat detection and enhanced security insights.

How does a big data SIEM differ from a traditional SIEM?

Big Data SIEM solutions use distributed computing, cloud-native architectures, and machine learning to analyze massive datasets in real time, while traditional SIEMs often rely on legacy databases that may not scale efficiently.

Experience The Next Generation SIEM With WeAnkor’s Services.

Contact us today to learn more about how we can elevate your business to new heights of efficiency,
security, and reliability

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