Exabeam vs. Splunk：比較・評価する6つの方法
ガイド
A framework improving security outcomes and reducing total cost of ownership
This guide compares Exabeam and Splunk across six key areas to help evaluate cost, detection, and investigation effectiveness.
Splunk is widely used for log management, but many teams face unpredictable pricing, complex tuning, and slow investigations as data and environments grow. Maintaining SPL queries, correlations, and add-ons can pull analysts away from resolving real threats. A behavior-led, AI-driven approach changes how you detect risk, investigate activity, and prioritize response across users, devices, and AI agents.
Key Questions This Guide Helps You Answer
- How does Splunk pricing impact total cost as data, compute, and add-ons scale?
- How much tuning and overhead does SPL and UEBA introduce?
- How does behavioral analytics improve detection of credential misuse and lateral movement?
- What changes when detection and investigation run in a unified platform?
- How can you measure detection coverage using MITRE ATT&CK®?
- How do AI agents improve investigation speed and analyst productivity?
How Does Exabeam Improve Security Operations Compared to Splunk?
New-Scale Fusion applies behavioral analytics and dynamic risk scoring to detect risk across users, devices, and AI agents. Automated timelines unify related activity into a single investigation flow, so analysts can understand how threats evolve without stitching together events.
A coordinated system of AI agents automates detection, investigation, and response. Analysts can search, analyze, prioritize, and create detection logic using natural language, with outputs aligned to the Common Information Model (CIM) and ATT&CK. Map detection coverage to use cases and easily see where to improve.
Download the guide to learn how to reduce cost, improve detection coverage, and accelerate investigations.
ガイドを入手するExabeam vs. Splunk - 6つの比較・評価方法
以下のフォームにご記入の上、送信してください。