How to build SentinelOne dashboards in Metabase

SentinelOne is an autonomous endpoint security platform whose Singularity agents detect and respond to threats across endpoints, cloud workloads, and identities. Metabase is where you turn those security signals into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the SentinelOne purple-mcp and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs SentinelOne findings into a database so you can build dashboards anyone can read.

How do you connect SentinelOne to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring security reporting to a warehouse-backed model.

What can you analyze from SentinelOne data in Metabase?

• Threat volume by classification — built from threats (detections) and the related agents (endpoints), sites, mitigations data your sync exposes.
• Mitigation status and unresolved threats — built from threats (detections) and the related agents (endpoints), sites, mitigations data your sync exposes.
• Agent coverage and health — built from threats (detections) and the related agents (endpoints), sites, mitigations data your sync exposes.
• Detection-to-mitigation time — built from threats (detections) and the related agents (endpoints), sites, mitigations data your sync exposes.
• Noisy exclusions — built from threats (detections) and the related agents (endpoints), sites, mitigations data your sync exposes.

Which SentinelOne dashboards should you build in Metabase?

How do you use the SentinelOne purple-mcp with the Metabase CLI?

Pair the SentinelOne purple-mcp with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for recent threats (detections) with severity, status, and detection timestamps.
• Export the result as CSV, keeping stable IDs, severities, statuses, owners, and timestamps.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Detection and triage timestamps are required for MTTD and time-to-triage; a single export can't show trend lines.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up SentinelOne MCP and the Metabase CLI?

{
  "mcpServers": {
    "sentinelone": {
      "command": "uvx",
      "args": [
        "--from", "git+https://github.com/Sentinel-One/purple-mcp",
        "purple-mcp"
      ],
      "env": {
        "PURPLEMCP_CONSOLE_TOKEN": "your-service-user-token",
        "PURPLEMCP_CONSOLE_BASE_URL": "https://your-tenant.sentinelone.net"
      }
    }
  }
}

Distributed through the GitHub repo (and AWS Marketplace) rather than a package registry — check the repo README for the current launch command. The server is read-only by design.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a threats-(detections) export — creates a table AND a model
mb upload csv --file sentinelone-threats-(detections).csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file sentinelone-threats-(detections).csv

Can you generate a SentinelOne dashboard with AI?

Yes. Use the prompt below with any assistant that can run the SentinelOne purple-mcp and the Metabase CLI. It works end to end: if SentinelOne tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for SentinelOne detection & response analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help security and engineering leaders understand detection volume, triage speed, sensor coverage, and response trends from SentinelOne data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for sentinelone tables and
  models). If durable SentinelOne data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the SentinelOne purple-mcp:
  threats (detections), plus agents (endpoints), sites, mitigations.
  Prefer aggregated or entity-grain views over raw events. Write each result to a
  CSV, then load it with the Metabase CLI — run "mb upload csv --file <export>.csv"
  so each upload creates a table and a ready-to-query model. Use "mb upload replace
  <table-id> --file <export>.csv" to refresh an existing table instead of creating
  duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, severities,
statuses, timestamps, and whether snapshots or history exist before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  SentinelOne — it reads a database or CLI-uploaded tables.
- Never try to load raw security telemetry or event firehoses into Metabase; use
  findings, detections, entities, and daily rollups.
- Only compute durations (time to remediate, time to triage, time to detect)
  when the required timestamps exist.
- Exclude test environments and suppressed or accepted-risk items from headline
  cards, and keep them visible in a labeled register instead.
- A single CSV is a point-in-time snapshot: only build trend cards if there is a
  usable date column or multiple periods have been uploaded.

Dashboard title: SentinelOne Detection & Response Overview

Sections:
1. Executive summary: Detections last 30 days; Open now; Median time to triage;
   False-positive rate; Sensor coverage.
2. Volume: Detections by week, severity, and tactic; repeat-offender hosts.
3. Triage: Untriaged by age; time-to-triage trend; auto-resolved share.
4. Coverage: Sensors vs. inventory; outdated or offline agents.
5. Response: Median time to detect; time to contain; incidents opened.

Filters: Date range, Severity, Status, Team or owner, Environment or asset group.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync SentinelOne data into a database or warehouse?

For dashboards that need history and reliability, land SentinelOne findings and metadata in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the SentinelOne Management API for control over fields, snapshot cadence, and refresh schedule.
• MCP + CSV — use this for quick exploration and one-off slices.

No managed ELT connector, and the full API reference sits behind the console and community login. Cloud Funnel streams raw telemetry to S3 or GCS — a security-lake feed, not a BI input. For Metabase, land threats, agents, and mitigations at entity grain via the Management API.

Notes

• Decide the snapshot cadence first (daily is the norm for security data) — posture trends only exist if you build the history.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize host, severity, tactic, status, detected-at, and triaged-at fields.

How should you model SentinelOne data in Metabase?

Core tables

Modeling advice

• Build a clean detections model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (assets, users, controls) from findings, events, and daily snapshot tables.
• Exclude test environments and suppressed or accepted-risk items from headline metrics; keep them in a labeled register instead.
• Use stable IDs for asset, user, and finding joins; display names change.

Which SentinelOne metrics should you track in Metabase?

What SQL powers SentinelOne dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  date_trunc('week', detected_at) AS week,
  severity,
  COUNT(*) AS detections,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (triaged_at - detected_at)) / 3600
  ) FILTER (WHERE triaged_at IS NOT NULL) AS median_triage_hours
FROM detections
GROUP BY 1, 2
ORDER BY 1, 2;

SELECT
  date_trunc('month', detected_at) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (detected_at - first_activity_at)) / 3600
  ) AS median_detect_hours,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (detected_at - first_activity_at)) / 3600
  ) AS p90_detect_hours
FROM detections
WHERE first_activity_at IS NOT NULL
GROUP BY 1
ORDER BY 1;

SELECT
  a.platform,
  COUNT(*) AS inventory_assets,
  COUNT(s.asset_id) AS assets_with_sensor,
  ROUND(
    100.0 * COUNT(s.asset_id) / NULLIF(COUNT(*), 0), 1
  ) AS coverage_pct
FROM assets a
LEFT JOIN sensor_inventory s
  ON s.asset_id = a.id
  AND s.last_seen_at >= CURRENT_DATE - INTERVAL '7 days'
GROUP BY a.platform
ORDER BY coverage_pct ASC;

What are common mistakes when analyzing SentinelOne in Metabase?

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FAQ