Skip to main content

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".

Informations de source

Dépôt
elastic/example-mcp-app-security
Dernière activité de la source
17 avril 2026 à 20:40
Langue détectée de SKILL.md
anglais
Étoiles
25
Forks
16

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
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.*` |
Voir sur GitHub