Microsoft Presideo
You set up microsoft presideo by running its text scanner and masking containers in your stack. These services find and replace private data like emails and card numbers in plain text. Most teams wire these scanners into their app code or log pipelines. Still, broad database roles still let users run direct queries that skip the app scanner. How microsoft presideo works in your stack This open-source tool uses two main services to handle private data in text. The first service scans text to f
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You set up microsoft presideo by running its text scanner and masking containers in your stack. These services find and replace private data like emails and card numbers in plain text. Most teams wire these scanners into their app code or log pipelines. Still, broad database roles still let users run direct queries that skip the app scanner.
How microsoft presideo works in your stack
This open-source tool uses two main services to handle private data in text. The first service scans text to find sensitive patterns like phone numbers or national IDs. It uses pattern matching rules and language models to spot these items. You send raw text to this scanner over a standard web call. The scanner sends back a list of matched items and their exact locations.
The second service takes those locations and changes the raw text. You can set rules to mask, hash, or replace each matched field. For example, the service can replace a credit card number with a generic tag. Masking rewrites matched fields in results so downstream apps only get clean text. Teams often deploy both services as containers inside their own cloud clusters. Apps then call these containers before saving text to a database or log store.
What the native setup solves and where gaps remain
Running these containers inside your app workflow solves a clear problem for builders. It keeps private strings out of log files, support tickets, and model training sets. Developers can call the service right inside their code before they write data. You can also add custom rules to match internal ID formats specific to your company.
Yet this app-level setup leaves a major gap in daily operations. The scanner only checks text that your app explicitly sends to its endpoint. It does not watch direct sessions when engineers log in to databases or servers. Database roles decide who can log in, not what individual queries return in flight. When an admin runs a query from a local tool, the scanner is bypassed. Sensitive emails, phone numbers, and card numbers flow back to the screen in plain text. You also have to run, scale, and update the scanner containers across every cluster.
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Running custom scanners without app code changes
Teams often want data masking to work across all tools, not just one app. Writing scanner calls into every script and legacy service takes too much time. If a team forgets one service, private data leaks into outputs or logs. Also, heavy text scanning can add wait time if containers are not sized well.
A better pattern separates data masking from your app code and user tools. Instead of asking every developer to call an endpoint, you apply rules in flight. This ensures that every query result gets checked, no matter what client tool runs it. Standard roles still handle the initial login, while live checks rewrite matched fields in results.
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 runs beside each supported service as an open-source proxy, a sidecar for your database or API.
First, hoop.dev guardrails block banned queries before they run. Then hoop.dev data masking rewrites matched sensitive values in responses in memory. The audit trail records every statement and verdict per resource. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
Why do people search for microsoft presideo with an e?
Many engineers type microsoft presideo as a common spelling mistake for the open-source data tool. Even with the typo, they are looking for the same Microsoft text scanner and masking engine.
What types of data can this tool rewrite?
The scanner finds structured fields like emails, phone numbers, credit card numbers, and national IDs. Once found, the masking service rewrites those matched fields in the text output.
Does the open-source scanner block direct database queries?
No, the native containers only process text sent to their web endpoints by your code. They do not intercept live database sessions or queries run from local developer tools.