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As adversaries leverage AI to accelerate attacks, security teams must operate with unprecedented speed and precision to protect the organization.
Splunk Enterprise Security (ES) Premier meets the agentic era head-on, unifying SIEM, SOAR, and UEBA into the industry’s most complete AI-powered SecOps platform. By integrating detection, investigation, and response (TDIR) into one fluid motion, we eliminate the tool-swiveling and toil that leads to analyst burnout.

• Leverage AI-guided workflows and automation to reduce toil and speed investigations
• Take advantage of the best analyst experience with unified TDIR workflows
• Focus on the most critical threats and reduce false positives with advanced detections and enriched alerts using Cisco Talos
• Detect insider threats across users, devices, and apps using machine learning

Observability for AI extends traditional monitoring to cover the full lifecycle of AI systems, providing unified visibility across models, prompts, orchestration, infrastructure, and user interactions. It enables teams to track not only performance and availability, but also the quality, cost, safety, and behavior of AI workloads in real time. By correlating telemetry with evaluation and security signals, organizations can quickly identify risks such as model drift, hallucinations, latency issues, and prompt-based attacks.

• Leverage AI-guided systems to remain reliable and transparent
• AI governance
• Maintaining control while scaling AI-driven experiences

The transition to an "Agentic-Ready" Security Operations Center (SOC) represents a fundamental shift from manual, high-friction investigation processes to an AI-augmented model designed for speed, clarity, and scalability. This approach leverages agentic features—specifically within the context of ES 8.4—to transform how analysts interact with security data.

• Securing autonomous AI-driven environments with advanced analytics and intent-aware controls
• Integration across Cisco and Splunk security technologies

Your organisation has invested years in Observability tooling, ITSM workflows, and operational data. Yet MTTR remains stubbornly high, the gap between insight and action still requires the right person to wake up at 3am.
AgenticOps changes this equation.
Imagine an autonomous operations agent that thinks in your telemetry, reasons across your entire toolchain, and acts — not just alerts. One that handles the toil, escalates with context, and keeps your team focused on decisions that actually need a human. Whether you're running Kubernetes in the cloud or a Java app stack managed by a NOC team with a runbook binder, the architecture is the same.

Everything you need is already there. So where do you start?

• Practical aspects of AgenticOps - what does a production-grade AgenticOps architecture actually look like?
• Agent decisions governance so your team stays in control
• The realistic path to achieve agentic operations you can trust