Splunk AI for Mature SIEM and Analytics Programs

In-Depth Review

Splunk has been the dominant enterprise SIEM platform for nearly two decades, processing security and operational data at scales that competitors have struggled to match. Following Cisco’s $28 billion acquisition in 2024, Splunk is layering AI capabilities across its platform while integrating with Cisco’s network security intelligence.

Splunk’s Strongest Capabilities

Risk-based alerting is Splunk’s most impactful innovation for security operations. Traditional SIEMs generate alerts for individual events — a failed login, an unusual process, a network anomaly — creating thousands of alerts per day. Risk-based alerting instead assigns risk scores to entities (users and assets) and only generates alerts when accumulated risk crosses a threshold. This means a single analyst alert might represent the correlation of a credential stuffing attempt, followed by anomalous VPN usage, followed by unusual data access — a coherent threat narrative rather than three separate low-priority alerts.

SPL (Search Processing Language) remains Splunk’s most powerful differentiator for experienced security analysts. No other SIEM query language provides the same depth of statistical, analytical, and transformation capabilities. Analysts who master SPL can build custom detections, conduct forensic investigations, and create operational dashboards that would be impossible in less flexible platforms.

Splunk SOAR (formerly Phantom) is the integrated orchestration and automated response platform, offering 300+ pre-built integrations with security tools. The visual playbook builder allows security teams to automate incident response workflows — from phishing triage to malware containment — without requiring programming skills. The combination of SIEM detection with native SOAR automation in a single platform is a significant operational advantage.

Known Trade-offs

Splunk’s ingestion-based pricing is its most persistent criticism. Organizations pay based on the volume of data they send to Splunk, which creates perverse incentives: security teams must balance the desire for comprehensive visibility against the cost of ingesting data from all relevant sources. In practice, this means many organizations make conscious decisions to exclude data sources that should be monitored, creating blind spots driven by budget constraints rather than risk assessment.

The AI capabilities being added to Splunk, while useful, feel incremental compared to platforms like Cortex XSIAM that were designed AI-first. The Splunk AI Assistant translates natural language to SPL and back, and ML-powered detections add anomaly detection, but the underlying platform architecture was built for human-driven query and correlation rather than autonomous AI-driven operations.

The Bottom Line

Splunk AI is the right choice for organizations with mature security operations teams, established SPL expertise, and existing Splunk infrastructure investments. The risk-based alerting and SOAR capabilities deliver genuine operational improvements. Organizations evaluating SIEM platforms for the first time should also consider cloud-native alternatives that may offer a faster path to AI-driven security operations without the data ingestion pricing model.