The Data Platform for Trusted AI at Scale

Analyze data where it lives at lower cost, connect data in real time across domains, and unlock machine data for model training and agentic action. 

 

The Splunk Platform powers the Cisco Data Fabric architecture, helping security, IT, network, engineering, data, and AI teams turn distributed machine data into trusted cross-domain context for model training and agentic action. 

 Key Platform Differentiators

  • Federate Data: Analyze data across Splunk and external stores without moving datasets.
  • Correlate at Scale: Connect data in real time across security, network, application, infrastructure, and third-party domains.
  • Transform Data for AI: Unlock machine data and operational context for AI-assisted investigations, predictive workflows, and agentic action with controls and oversight.

Capabilities

  • Activate AgenticOps to move from reactive to predictive workflows.
  • Build trusted, agentic workflows that predict issues, accelerate resolution, and automate responses.
  • Deploy AI assistants and agents to transform machine data into resilient operations across security and observability.
  • Unify telemetry across security, infrastructure, and networks for comprehensive context.
  • Observe and evaluate AI agents and infrastructure with Splunk Agent Observability.
  • Speed insights and action with Splunk AI Assistant, reducing manual work and speeding resolution.
  • Build and deploy AI models with guided workflows using AI Toolkit.
  • Activate agents and MCP Server for guided resolution and coordinated workflows.
  • Accelerate investigations with AI Canvas, enabling collaborative, iterative execution cycles.
  • Maximize data value by enhancing visibility, uncovering insights, and reducing costs.
  • Address challenges like overwhelming data volumes, tightening compliance, and increasing costs.
  • Reduce data management costs with tailored data flow, filtering, redacting, and routing.
  • Enjoy flexible data access and improved search performance.
  • Break down silos for richer context and faster issue resolution.
  • Unified analytics across hybrid data environments.
  • Query distributed data in place without moving or duplicating it.
  • Intelligent, scalable data preparation with filtering, masking, transformation, and aggregation.
  • Use SPL2 query language for unified analytics across streaming and historical data.
  • Automated schema resilience to handle schema drift and ensure consistent query results.
  • Connect AI assistants, agents, and LLMs with Splunk platform data.
  • Enable AI models and LLMs to securely access Splunk data using natural language.
  • Accelerate workflows across security, DevOps, and business insights.
  • Secure integration with enterprise systems with access controls and auditing.
  • Automate log analysis, real-time alerts, and metadata retrieval.
  • Retain full-fidelity machine data economically at petabyte scale.
  • Search data in place and promote important data to Splunk indexes for high-performance analytics.
  • Preserve raw detail, governance, and context for investigations and AI.
  • Discover, understand, and govern retained machine data with Catalog.
  • Promote data when speed matters for real-time detections and dashboards.