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detection-rule-management

Create, tune, and manage Elastic Security detection rules. Use for false positive tuning, adding exceptions, creating new detection coverage, finding noisy rules, enabling/disabling rules, or any detection engineering task. Also trigger for "detection rules", "noisy rules", "false positives", "add exception", "create rule", or "tune rule".

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elastic/example-mcp-app-security
Última actividad en el origen
17 de abril de 2026 a las 20:40
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25
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SKILL.md
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name
detection-rule-management
description
Create, tune, and manage Elastic Security detection rules. Use for false positive tuning, adding exceptions, creating new detection coverage, finding noisy rules, enabling/disabling rules, or any detection engineering task. Also trigger for "detection rules", "noisy rules", "false positives", "add exception", "create rule", or "tune rule".
# Detection Rule Management Manage detection rules using the `elastic-security` MCP connector. The `manage-rules` tool renders an interactive rule management dashboard. ## Tools (via elastic-security MCP connector) | Tool | Purpose | |------|---------| | `manage-rules` | Browse/search rules with interactive dashboard. Params: `filter` (KQL) | | `threat-hunt` | Test queries against live data before creating rules | The dashboard supports searching rules, viewing details, enabling/disabling, validating queries, and viewing noisy rules. ## Rule Types | Type | Use case | Example | |------|----------|---------| | `query` (KQL) | Simple field matching | `process.name: "mimikatz.exe"` | | `eql` | Behavioral sequences | Process A spawns B within 5 minutes | | `esql` | Analytics/aggregations | Complex joins or transformations | | `threshold` | Count/frequency | >10 failed logins in 5 minutes | | `threat_match` | IOC correlation | Match against malicious IP indicators | | `new_terms` | First-time activity | User logs into host for first time | ## Tuning Strategy (in order of preference) 1. **Add exception** — Known-good process/user/host. Does not modify the rule query. 2. **Tighten the query** — Exclude FP pattern from the rule query itself. 3. **Adjust threshold/suppression** — Increase threshold or enable alert suppression. 4. **Reduce risk score/severity** — Downgrade priority if rule has some value but is noisy. 5. **Disable the rule** — Last resort. Only if rule provides no value. ## Creating New Rules 1. Define the threat (MITRE technique, data sources, malicious vs legitimate behavior) 2. Test the query with `threat-hunt` against live data 3. Create via the dashboard or ask Claude to help construct the rule JSON 4. Monitor alert volume and tune false positives ## Common Index Patterns | Data type | Index pattern | |-----------|--------------| | Alerts | `.alerts-security.alerts-*` | | Processes | `logs-endpoint.events.process-*` | | Network | `logs-endpoint.events.network-*` | | Windows | `logs-windows.*` | | AWS | `logs-aws.*` | | Okta | `logs-okta.*` |
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