The platform your AI and your SOC actually agree on

An agent plans and acts — it decides what to do next, calls tools, and loops until the task is done. A skill is what it knows how to do. In Elastic Security, one agent, the Elastic AI Agent, can run all available skills: alert analysis, entity analytics, threat hunting, anomaly detection, detection rule editing, automatic troubleshooting, and more as the catalog grows.

It's the cost of trusting AI you can't see into — vendor-mandated models with no transparency, no model choice, and no way to validate decisions made on your behalf. When AI is making security decisions about triage, investigation, and response, and you can't audit its logic, that's not automation. That's risk.

Elastic is model-agnostic — bring your own LLM or run one on-premises for air-gapped environments. Every AI decision is transparent and auditable: see the prompts, inspect the queries, edit the workflows. Elastic Security Labs threat intelligence feeds directly into the skills, so you know exactly what the agent is reasoning from. No hidden logic, no vendor lock-in.

Elastic Security uses agentic AI to detect, investigate, and respond — not just flag. When an alert fires, the agent fetches context, correlates related alerts by shared entities, queries Elastic Security Labs threat intelligence, and recommends a disposition. Analysts get a finished case, not a queue of raw alerts.

Yes. Elastic's built-in security skills are composable and can invoke each other. You can also build custom skills in Agent Builder — package your own instructions and give the agent capabilities specific to your environment. Open detection rules, open schema, and a public MCP server mean no lock-in.

Always. The agent investigates, correlates, and stages a response — the analyst reviews and approves. Elastic's agentic security operations model puts humans at the top of the loop, not out of it. Every AI decision is transparent and auditable.