Splunk AI is an integrated suite of AI-powered solutions designed for security and observability.

Introduction

Splunk AI is an integrated suite of AI-powered solutions designed for security and observability. It is not a single tool but rather a core part of the Splunk platform, built to help security, IT, and engineering teams detect, investigate, and respond to incidents faster. By leveraging machine learning and generative AI, Splunk AI transforms vast volumes of machine data into actionable insights.

The platform’s AI capabilities are purpose-built to augment human intelligence, helping teams separate critical signals from noise, predict outages before they occur, and accelerate troubleshooting across complex digital ecosystems.

AI-Powered

AI-Native Data Platform

Security & Observability

Human-in-the-Loop

Predictive Analytics

Review

Splunk AI is known for its powerful and deeply integrated AI capabilities, which are specifically designed for the mission-critical domains of security and observability. The platform’s primary strength is its AI-native data platform, which processes machine data at scale to provide real-time insights with unparalleled precision.

The AI assistant for Search Processing Language (SPL) is a standout feature, democratizing data analysis by translating natural language into complex queries. While the learning curve can be steep and the pricing model is complex, the value it provides in reducing alert noise, predicting incidents, and accelerating a Mean Time to Respond (MTTR) is significant. Splunk AI is an indispensable tool for any enterprise seeking to build digital resilience and leverage AI to operate more securely and reliably.

Features

AI Assistant for SPL

A generative AI-powered assistant that translates natural language into complex Search Processing Language (SPL) queries. It makes data analysis more accessible to users of all skill levels.

AI-Driven Incident Prediction (AIOps)

Uses machine learning to identify anomalies, correlate events, and predict future incidents, helping to proactively prevent outages.

AI for Security

AI capabilities are embedded in security products to summarize findings, generate contextual reports, and provide workflow validations for faster incident response.

AI for Observability

An AI assistant helps troubleshoot by analyzing traces, logs, and metrics, pinpointing root causes and reducing mean time to resolution (MTTR).

Machine Learning Toolkit (MLTK)

A free application that allows users to build, train, and deploy their own custom machine learning models on their Splunk data.

Smart Analytics

AI-powered features like sentiment analysis and anomaly detection help teams extract meaningful insights from unstructured data.

Best Suited for

Security Operations (SecOps) Teams

Ideal for threat detection, incident investigation, and automating security playbooks.

IT Operations (ITOps) Teams

Excellent for monitoring application health, predicting outages, and reducing alert noise.

Site Reliability Engineers (SREs)

Perfect for accelerating troubleshooting, finding root causes of performance issues, and optimizing service delivery.

Data Scientists

A valuable platform for building and deploying custom machine learning models on a vast, unified dataset.

DevOps & Engineering Teams

Helps with code analysis, performance monitoring, and ensuring application reliability.

Enterprise-level Businesses

A comprehensive, scalable solution for large organizations with complex digital environments and high data volumes.

Strengths

The platform is built to handle petabytes of data, making it the industry leader for large-scale security and observability needs.

Splunk’s AI philosophy emphasizes a “human-in-the-loop” approach and domain-specific models, ensuring that the AI is trustworthy.

The platform is highly extensible, allowing users to apply their own custom AI models and integrations to fit specific business needs.

The predictive analytics and monitoring capabilities help organizations proactively maintain system health.

Weakness

The power of Splunk’s platform and its Search Processing Language (SPL) can be complex for new users.

While highly flexible, managing and optimizing data ingestion can be a complex and resource-intensive process.

Getting started with: step by step guide

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Step 1: Data Ingestion

The platform begins by ingesting vast amounts of machine data from every part of a digital ecosystem, including logs, metrics, and traces.

Step 2:AI-Powered Analysis

The AI-native platform automatically analyzes this data to identify anomalies, reduce alert noise, and predict potential issues.

Step 3: Human-in-the-Loop Interaction

Users leverage the AI Assistant for natural language queries, allowing them to ask complex questions and receive simplified answers or tailored SPL queries.

Step 4: Investigation and Response

The AI assistant helps teams investigate incidents by providing summaries and relevant context, helping to pinpoint the root cause and accelerate response.

Step 5: Proactive Optimization

Teams use the predictive insights and reports generated by the AI to make data-driven decisions, prevent future outages, and optimize system performance.

Frequently Asked Questions

Q: What kind of AI does Splunk use?

A: Splunk uses a combination of machine learning (ML) for features like anomaly detection and predictive analytics, and generative AI for its AI Assistant and contextual insights. The AI assistant is powered by Google’s Gemini LLM.

Q: How does the AI assistant for SPL work?

A: The AI assistant uses natural language processing to understand a user’s request (e.g., “Show me the top 5 hosts with the most errors”) and translates it into a precise, executable Splunk Search Processing Language (SPL) query.

Q: Is Splunk AI focused on security or observability?

A: Splunk AI is purpose-built for both. Its AI-native platform provides distinct solutions for security operations (SecOps) and observability, with shared data and workflows for a unified experience.

Pricing

Splunk’s pricing model is not a simple, fixed monthly fee. Instead, it is a flexible and transparent system based on either data ingestion volume or workload consumption. This tiered approach is designed to scale with an organization’s needs, from defined IT and security use cases to full enterprise-wide deployments. For a precise estimate, businesses must contact the sales team, but a free trial is available to test the platform.

Basic

Ingest or Workload Pricing

Organizations with defined data volumes or specific compute-intensive workloads.

Standard

Ingest or Workload Pricing

Businesses focused on modernizing their security operations with advanced analytics.

Pro

Ingest or Workload Pricing

Teams that need to monitor application performance and infrastructure with granular control.

Alternatives

Datadog

A cloud-based monitoring and security platform that offers a unified view of metrics, traces, and logs with an extensive integration ecosystem.

Microsoft Sentinel

A cloud-native security information and event management (SIEM) solution that uses AI to detect and respond to threats.