IBM Security Network IPS and Splunk User Behavior Analytics compete in cybersecurity, with IBM offering competitive pricing and support. However, Splunk is seen as superior due to its advanced features, justifying its higher cost.
Features: IBM Security Network IPS excels in intrusion prevention, comprehensive security protocols, and real-time threat analysis. Its strength lies in threat prevention. Splunk User Behavior Analytics offers user behavior analysis, anomaly detection, and insights into insider threats. It focuses on gaining insights from user behavior.
Room for Improvement: IBM Security Network IPS could improve in integrating more advanced analytical tools, enhancing user interface design, and expanding automation capabilities. Splunk User Behavior Analytics might benefit from reducing its initial setup costs, improving user training resources, and enhancing scalability for larger deployments.
Ease of Deployment and Customer Service: IBM Security Network IPS is known for straightforward deployment and responsive customer service. Splunk User Behavior Analytics offers a flexible deployment model with extensive support for complex environments, focusing on comprehensive, tailored support.
Pricing and ROI: IBM Security Network IPS is cost-effective with a lower setup cost, offering substantial ROI through robust security measures. Splunk User Behavior Analytics, although more expensive initially, is justified by detailed analytics offering potential high ROI through enhanced security management.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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