Exabeam vs. Splunk: Which Approach Improves Security Operations Outcomes?

Not every SIEM takes the same approach to modern security operations. Splunk is widely used for log management, but teams may face pricing complexity, ongoing SPL maintenance, and fragmented investigation steps as environments grow.

New-Scale Fusion combines behavioral analytics, dynamic risk scoring, measurable detection coverage, and coordinated Exabeam Nova AI agents. The result is a connected workflow designed to help teams identify credible risk earlier and move from detection through investigation and response with less manual effort.

Here are six differences to consider.

1. Predictable Costs and Clear Value

Splunk pricing can depend on ingestion, storage, compute, workload, and packaged capabilities. That combination can make cost forecasting harder as data volumes and use cases change. New-Scale Fusion brings behavioral analytics, automation, threat intelligence, and AI-assisted workflows together in one platform, giving teams a more predictable path to expand capabilities.

Outcome: A more predictable path to increasing security operations value.

2. Less Tuning and Operational Overhead

Splunk environments often require specialized SPL expertise, correlation-rule maintenance, behavioral-baseline management, and continuous false-positive reduction. New-Scale Fusion provides prebuilt detection coverage, behavioral analytics for human and non-human identities, contextual correlation, and automated investigation workflows. Analysts can move from a detection to supporting evidence without building every step by hand.

Outcome: Faster time to value and less dependence on specialized engineering resources.

3. Behavioral Analytics Connects Human and Agent Risk

Credential misuse, lateral movement, insider activity, and agent misuse can look legitimate when events are reviewed one at a time. Exabeam uses machine-learned user and entity behavior analytics (UEBA), Agent Behavior Analytics (ABA), dynamic risk scoring, and automated search timelines to evaluate behavior over time. ABA extends the same behavior-led model to AI agents and non-human identities. Timelines then assemble related human, device, service-account, and agent activity into a sequence analysts can investigate.

Splunk’s public UEBA documentation discusses behavioral baselines for users and assets. It does not currently describe a native pre-built ES UEBA model in which an AI agent has its own learned behavioral baseline, nor does it document behavioral-drift scoring for AI agents.

ABA delivers agent baselines, drift detection, unusual tool-use detection, and human-to-agent context as a defined product capability.

Outcome: Earlier identification of slow-moving risk with connected investigation context.

4. Cloud-Native Security Operations at Scale

New-Scale Fusion unifies ingestion, parsing, analytics, detection, and investigation in a cloud-native foundation. It also provides visibility into service health and consumption and uses OpenAPI Standard (OAS) to connect with security and IT ecosystems.

Outcome: A scalable foundation that keeps data; analytics; and threat detection, investigation, and response (TDIR) workflows connected.

5. Measurable Detection Coverage

Splunk Security Essentials offers security content and MITRE ATT&CK^® mapping, but teams may still need custom content and reporting to assess program-level maturity. Outcomes Navigator maps coverage to ATT&CK and business use cases, identifies gaps, and prioritizes improvements. Behavioral analytics and dynamic risk scoring help teams focus those improvements on credible risk.

Outcome: A measurable view of detection coverage, gaps, and next steps.

6. Nova AI Adds Guided Investigation

Splunk’s AI SOC Analyst automates investigations around findings. Nova AI can investigate cases and alerts but can also look up users, hosts, and IP addresses, review associated activity timelines, and preserve investigation context as analysts move between views. This distinction matters because an insider threat may not appear as a single high-severity alert; the evidence may be distributed across multiple entities, events, and time periods.

Nova AI coordinates seven AI agents that assist with posture guidance, search, visualization, threat scoring, investigation, analyst support, and rule creation.

Exabeam Nova Guided Investigations extends that assistance into a persistent, platform-wide co-investigator. It gathers background context, runs supporting searches, helps triage cases, and preserves conversational context as analysts move through the platform. Analysts validate the findings and retain control of response decisions.

Outcome: Faster, more consistent investigations with less manual context gathering.

Choose the Approach That Fits Your Environment

Replacing Splunk is not the only path. New-Scale Analytics can augment an existing Splunk deployment with behavioral detection, dynamic risk scoring, timelines, and Nova AI-assisted investigation while Splunk remains the log collection and storage layer.

For teams ready to unify data management, analytics, and TDIR workflows, New-Scale Fusion provides the broader platform approach. In either model, Exabeam adds a behavior-led investigation layer that connects risk across human and agent activity.

Download the full comparison guide to evaluate cost, operations, behavioral detection, architecture, coverage, and AI-assisted investigation.