DNIF HYPERCLOUD and AlienVault OSSIM compete in the SIEM platform category. DNIF HYPERCLOUD has the upper hand due to its scalability, advanced analytics, and superior customer service.
Features: DNIF HYPERCLOUD users highlight its fast data processing, machine learning capabilities, and scalability. AlienVault OSSIM is praised for its broad range of built-in security features, including asset discovery, vulnerability assessment, and intrusion detection.
Room for Improvement: Users of DNIF HYPERCLOUD often request more intuitive configuration options and better documentation. AlienVault OSSIM users suggest improvements in performance and the addition of more user-friendly automation options.
Ease of Deployment and Customer Service: DNIF HYPERCLOUD offers a streamlined deployment process and robust customer support. AlienVault OSSIM's deployment can be more complex but is balanced by a supportive community.
Pricing and ROI: DNIF HYPERCLOUD may involve higher initial setup costs but provides significant ROI through its advanced capabilities. AlienVault OSSIM is cost-effective to set up but ROI may vary based on user expertise and needs.
AlienVault OSSIM, Open Source Security Information and Event Management (SIEM), provides you with a feature-rich open source SIEM complete with event collection, normalization and correlation. Launched by security engineers because of the lack of available open source products, AlienVault OSSIM was created specifically to address the reality many security professionals face: A SIEM, whether it is open source or commercial, is virtually useless without the basic security controls necessary for security visibility.
DNIF HYPERCLOUD is a cloud native platform that brings the functionality of SIEM, UEBA and SOAR into a single continuous workflow to solve cybersecurity challenges at scale. DNIF HYPERCLOUD is the flagship SaaS platform from NETMONASTERY that delivers key detection functionality using big data analytics and machine learning. NETMONASTERY aims to deliver a platform that helps customers in ingesting machine data and automatically identify anomalies in these data streams using machine learning and outlier detection algorithms. The objective is to make it easy for untrained engineers and analysts to use the platform and extract benefit reliably and efficiently.
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