Elastic Prometheus
You set up an elastic prometheus pipeline by forwarding scraped metrics to an Elastic cluster. Teams use remote write or an Elastic agent to store and query metric data. Engineering teams often build this setup to keep logs and metrics in one place. To move fast, admins often grant broad cluster roles to scraping agents and engineers. When roles are too wide, one leaked key exposes your entire index history. Users with broad read and write rights can also alter or delete production data. How a
Elastic Security + Prometheus Security: the complete guide
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You set up an elastic prometheus pipeline by forwarding scraped metrics to an Elastic cluster. Teams use remote write or an Elastic agent to store and query metric data. Engineering teams often build this setup to keep logs and metrics in one place. To move fast, admins often grant broad cluster roles to scraping agents and engineers. When roles are too wide, one leaked key exposes your entire index history. Users with broad read and write rights can also alter or delete production data.
How an elastic prometheus setup works
Prometheus pulls numeric data from your apps on a fixed time schedule. It stores these points on a local disk for short term alerts. Still, local storage fills up fast when you run many hosts and services. Teams add Elastic to hold months of history and run deep searches.
You can link the two tools in two main ways. First, you can run an agent on each host to scrape targets. The agent reads the local endpoint and sends the points to your search cluster. Second, you can set up the native remote write feature on your metric server. This method pushes every scraped batch straight to the search cluster intake port.
Both paths require an API key or a user account with write rights. The search cluster checks these keys before it accepts any new metric batches. Once the data lands in an index, engineers log in to view dashboards. Admins create roles in the search cluster to control who can read each index. They also map team groups to those roles to manage daily logins.
What native roles solve and what they miss
Native cluster roles do a good job of blocking unknown users at the door. You can split read rights and write rights across different index names. A scraping agent gets a role that only writes to metric storage. A developer gets a role that only reads from dashboards and search views. This basic split stops an agent key from reading other business indices.
Still, native setup rules only check who logs in at the start. They do not inspect what individual queries or commands do in flight. Once an engineer has a valid session, the role allows any query on that index. A user can run a heavy search that slows down the whole cluster. They can also pull sensitive labels that slipped into your metric tags.
Elastic Security + Prometheus Security: architecture patterns and best practices
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App teams often put user emails or phone numbers into custom metric labels by mistake. When those tags land in the search cluster, anyone with read rights sees them. Native roles cannot rewrite matched fields in results for emails or card numbers on the fly. Admins also struggle to track which person ran a specific query using shared keys. Teams often share one admin role during an outage to fix issues fast. That shared key leaves you with no clear audit log of who changed what.
How to tighten metric and search access
To keep your metric pipeline safe, start by shrinking every role you use. Give each scraping agent its own key with write rights to one stream. Never reuse human login keys inside your automated metric shipping jobs. Rotate those machine keys on a set schedule so old keys expire.
Next, clean up your metric labels before they leave the source app. Strip out high cardinality tags like national IDs, emails, and phone numbers early. This step keeps your storage costs low and protects user privacy at the same time. Finally, stop sharing static cluster passwords among your on-call staff during incidents. Require every engineer to use their own identity for every search and change.
Govern statements and responses across databases and APIs
Good config and scoped roles are needed setup. They do not check single queries in flight or mask sensitive fields in returned rows. Across Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, and internal HTTP endpoints, hoop.dev sits inline in the protocol beside each supported service as an open-source proxy, a sidecar for your database or API. For every query or command an AI agent or engineer runs against production, hoop.dev decides to pass, mask, block, or hold it for human approval.
First, hoop.dev guardrails block banned queries before they run or hold risky ones for review, and hoop.dev data masking rewrites matched sensitive values in responses in memory. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
Should I use remote write or an agent for elastic prometheus data?
Use remote write if you already run a stable metric server in your cluster. Choose the agent if you want one tool to ship both logs and metrics.
How do I stop sensitive data from entering my metric indices?
Review your app code to ensure developers never put emails or phone numbers in labels. You can also drop bad tags during the scrape step before shipping data.
Why do shared cluster keys cause audit problems for teams?
A shared key logs every query and index change under one generic account name. When an index breaks, you cannot tell which team member ran the bad command.