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securityclaw-autonomous-soc-agent

Detect and triage threats from OpenSearch or Elasticsearch log data with RAG context.

11|1|Updated May 16, 2026
Search Tags:#llm#rag#langgraph#anomaly detection#threat hunting#opensearch#soc
One-click install
npx skills add https://github.com/Aradotso/security-skills --skill securityclaw-autonomous-soc-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: securityclaw-autonomous-soc-agent
Source: https://github.com/Aradotso/security-skills/tree/main/skills/securityclaw-autonomous-soc-agent
Command: npx skills add https://github.com/Aradotso/security-skills --skill securityclaw-autonomous-soc-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Security teams need continuous detection and investigation across security log data, but manual triage and analyst workflows cannot keep up with fast-changing threats and noisy alerts.

Core Features & Use Cases

  • RAG-based behavioral memory: Builds and queries embeddings of normal network behavior stored in OpenSearch/Elasticsearch for contextual detection.
  • LLM anomaly analysis: Uses LLM validation to analyze anomalies with retrieved context and produce investigation-ready outputs.
  • LangGraph orchestrated SOC automation: Runs a DECIDE→EXECUTE→EVALUATE supervisor loop with conversation-based investigations and checkpointing.
  • Operational interfaces: Provides both CLI and a web UI/API to support real-time threat analysis and automation.
  • Provider flexibility: Supports OpenSearch vs Elasticsearch and Ollama vs other LLM providers.

Example use case

Monitor OpenSearch indices for near-real-time network anomalies, retrieve baseline context for the involved events, and automatically generate an analyst-style threat triage and investigation plan.

Quick Start

Deploy the SecurityClaw service by running the command: python main.py service.

Frequently Asked Questions about securityclaw-autonomous-soc-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate SOC threat triage from OpenSearch or Elasticsearch logs?▼

Autonomous SOC threat triage is automated by polling OpenSearch or Elasticsearch log data for anomalies, enriching context via RAG behavioral memory, and validating threats with an LLM to produce investigation-ready outputs.

How does RAG behavioral memory work for anomaly detection in network logs?▼

RAG behavioral memory works by building and querying vector embeddings of normal network behavior stored in OpenSearch or Elasticsearch, providing contextual baselines to detect and analyze network anomalies accurately.

Can I use LangGraph orchestration for automated security investigation workflows?▼

LangGraph orchestration can be used for automated security investigations by running a DECIDE, EXECUTE, and EVALUATE supervisor loop that manages conversation-based investigations with checkpointing.

Does this autonomous SOC agent support both Ollama and other LLM providers?▼

The autonomous SOC agent supports provider flexibility, allowing you to choose between Ollama and other LLM providers, and also between OpenSearch and Elasticsearch for log data storage.

What is the best way to monitor OpenSearch indices for near-real-time network anomalies?▼

The best way to monitor OpenSearch indices for near-real-time network anomalies is deploying an autonomous SOC service that polls logs, retrieves baseline context, and generates LLM-based threat triage plans.

Do I need a web UI or CLI to run autonomous security operations monitoring?▼

You can run autonomous security operations monitoring using either a web UI and API or a CLI interface, both of which support real-time threat analysis and automation workflows out of the box.

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