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

Not every SIEM solution is built for modern security operations. While Splunk is widely used for log management, many teams face unpredictable pricing, complex tuning, and slow investigations as environments scale.

New-Scale Fusion takes a different approach, It combines behavioral analytics, dynamic risk scoring, and coordinated AI agents to help teams detect risk earlier and move investigations forward faster.

Here are six ways Exabeam improves outcomes compared to Splunk.

1. Predictable Costs and Clear Value

Splunk cloud workload pricing model is complex and often unpredictable. Costs scale based on ingestion, storage, compute, and add-ons, which can lead to overages and rising spend as environments grow.

Exabeam includes analytics, automation, and threat intelligence within New-Scale Fusion. As data volume increases, you gain more detection coverage and automation without layering on additional modules.

Outcome: More predictable cost structure with increasing value over time.

2. Less Tuning and Operational Overhead

Splunk requires continuous tuning. Analysts must maintain Splunk Search Processing Language (SPL) queries, build correlations, and reduce false positives through manual adjustments.

Behavioral analytics capabilities are bundled into higher-tier offerings, increasing cost and complexity. Migration paths between legacy and newer capabilities can introduce additional operational risk.

Exabeam reduces this overhead with:

• Prebuilt detection coverage
• Behavioral analytics for users, entities, and agents
• Contextual correlation and automated workflows

Analysts move from detection to investigation in a single interface without custom scripting.

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

3. Strong Behavioral Visibility Across Humans, Devices, and Agents

Most modern attacks involve credential misuse or lateral movement. Rules alone are not enough to detect these patterns.

Splunk provides behavior-based detection, but it often requires extensive tuning and operates outside a unified investigation workflow.

Exabeam applies:

• Machine learning-based user and entity behavior analytics (UEBA)
• Dynamic risk scoring
• Agent Behavior Analytics (ABA)
• Automated timelines

ABA extends behavioral analytics to AI agents and non-human identities. It correlates activity across users, devices, and agents into a single investigation flow so analysts can understand how behaviors interact within an attack sequence.

While Splunk can monitor agent activity and assign risk scores, it approaches this from an observability perspective. Exabeam integrates agent behavior directly into the investigation workflow, so all related activity is analyzed together.

Outcome: Faster identification of high-risk behavior with unified investigation context.

4. Cloud-Native Architecture Built for Scale

Splunk Cloud is a managed version of an architecture originally designed for on-premises deployments. Scaling often requires planning for storage, compute, and performance constraints.

New-Scale Fusion is cloud native. It:

• Processes high event volumes at scale
• Unifies ingestion, parsing, analytics, and detection
• Provides real-time visibility into service health and consumption

OpenAPI Standard (OAS) support enables integration with broader security and IT ecosystems.

Outcome: Consistent performance and visibility as data and complexity grow.

5. AI Agents That Drive Investigation and Detection Outcomes

Splunk AI Assistant focuses on generating SPL queries and summaries. Its use is limited and often dependent on specific configurations.

Exabeam Nova is a coordinated system of seven AI agents embedded into the detection, investigation, and response workflow:

• Advisor Agent: Provides posture insights, MITRE ATT&CK^® alignment, and prioritized recommendations through Outcomes Navigator
• Search Agent: Converts natural language into Exabeam Query Language (EQL) aligned to the Common Information Model (CIM)
• Visualization Agent: Build dashboards and charts from search results
• Threat Scoring Agent: Applies adaptive learning to prioritize activity with dynamic risk scores
• Investigation Agent: Generates summaries and recommends next steps
• Analyst Assistant Agent: Surfaces evidence and guides investigators in real time
• Rule Creator Agent: Translates natural language and observed attack patterns into correlation rules aligned to CIM and ATT&CK to accelerate detection coverage

These agents work together within a unified workflow, reducing manual effort and improving investigation consistency.

Outcome: Faster investigations, improved prioritization, and reduced analyst workload.

6. Measurable Detection Coverage With Outcomes Navigator

Security teams often lack visibility into whether their SIEM is detecting meaningful threats and how gaps translate to business risk.

Splunk Security Essentials (SSE) is a content library that helps teams implement security use cases and map detections to frameworks like ATT&CK. While it highlights detection gaps, it provides limited visibility into exposure and only indirect insight into business risk.

Outcomes Navigator delivers:

• Detection coverage mapped to ATT&CK
• Visibility into gaps and exposure
• Prioritized recommendations aligned to business outcomes

It continuously tracks detection performance and program maturity without requiring custom dashboards.

Outcome: Clear visibility into detection coverage and next steps to reduce risk.

Conclusion

Log search alone can’t meet the demands of modern security operations.

Exabeam combines behavioral analytics, dynamic risk scoring, automated timelines, and AI agents to detect hidden risk in human, device, and agent activity.

You gain a unified investigation workflow, faster response times, and measurable improvements in your security program without needing to rebuild your entire stack.

Download the full comparison guide to see how to reduce cost, improve detection coverage, and accelerate investigations.