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Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
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| implementing-endpoint-detection-with-wazuh | Deploy and configure Wazuh SIEM/XDR for endpoint detection including agent management, custom decoder and rule XML creation, alert querying via the Wazuh REST API, and automated response actions. | cybersecurity | security-operations |
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1.0 | mahipal | Apache-2.0 |
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Implementing Endpoint Detection with Wazuh
Overview
Wazuh is an open-source SIEM and XDR platform for endpoint monitoring, threat detection, and compliance. This skill covers managing agents via the Wazuh REST API, creating custom decoders and rules in XML for organization-specific detections, querying alerts, and testing rule logic using the logtest endpoint.
When to Use
- When deploying or configuring implementing endpoint detection with wazuh capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Wazuh Manager 4.x deployed with API enabled
- Python 3.9+ with
requestslibrary - API credentials (username/password for JWT authentication)
- Understanding of Wazuh decoder and rule XML syntax
Steps
Step 1: Authenticate to Wazuh API
Obtain JWT token via POST to /security/user/authenticate.
Step 2: List and Monitor Agents
Query agent status, versions, and last keep-alive via /agents endpoint.
Step 3: Query Security Alerts
Search alerts by rule ID, severity, agent, or time range.
Step 4: Test Custom Rules with Logtest
Use the /logtest endpoint to validate decoder and rule logic against sample log lines.
Expected Output
JSON report with agent inventory, alert statistics, rule coverage, and logtest validation results.