Opendlp Open Source
You set up opendlp open source by deploying a central manager and target agents. Then you define regular expression rules to scan files and databases for sensitive data. Security teams run these scans to find unprotected card numbers or national IDs. Still, running deep scans requires broad read permissions across every target server and database. Granting wide read roles to a central scanner creates a high-value target for attackers. How an opendlp open source architecture works The tool use
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You set up opendlp open source by deploying a central manager and target agents. Then you define regular expression rules to scan files and databases for sensitive data. Security teams run these scans to find unprotected card numbers or national IDs. Still, running deep scans requires broad read permissions across every target server and database. Granting wide read roles to a central scanner creates a high-value target for attackers.
How an opendlp open source architecture works
The tool uses a two-part architecture to inspect data at rest across your network. A central web server holds the scan policies, target credentials, and result reports. Lightweight agents run on target hosts or query databases over the network. You start a scan by picking a policy and supplying host login keys.
Next, the central server pushes the policy and agent software to the targets. The agent reads local files, directory shares, or database tables row by row. It checks every text string against your regular expressions for emails or phone numbers. When the agent finds a match, it sends a report back to the server.
Teams schedule these jobs to run during off-peak hours to avoid high CPU load. The central dashboard collects the findings so analysts can review exposed records. After the review, admins must manually move files or change database table permissions.
What batch discovery solves and what it misses
Agent-based scanners are great at finding forgotten files and old database tables. They give compliance teams a clear snapshot of where sensitive records sit today. If a developer dumps card numbers into a test table, the next scan catches it. This visibility helps teams clean up unencrypted files and lock down shared drives.
Still, periodic scanners only check data at rest during scheduled batch windows. They do not watch live queries or commands that users run between scans. Once an engineer logs in with a database role, the scanner cannot stop a query. A user can still pull thousands of phone numbers or national IDs in seconds.
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Batch tools also struggle with the permissions needed to do their own jobs. To read every table, the scanner needs a role with full read rights. Storing those master keys on a central server adds risk to your setup. Also, native database roles only decide who logs in to the server. They do not inspect what individual queries return in flight or mask matched fields.
Managing sensitive data during live queries
Relying only on rest scans leaves a gap during daily engineering work. Developers and support staff need live database sessions to fix bugs and check apps. If you lock down tables after a scan, engineers lose the access they need. If you leave tables open, raw emails and card numbers flow to user laptops.
To fix this, teams pair rest discovery with controls that act on live traffic. Instead of waiting for a weekly scan, runtime controls inspect results as they leave. Masking rewrites matched fields in results so raw card numbers do not reach clients. This approach keeps engineers productive while protecting sensitive fields during every single query.
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
What types of data can opendlp open source find?
It finds structured text patterns like card numbers, national IDs, emails, and phone numbers. It uses regular expressions to match these patterns inside files and database tables.
Does an agent scanner block live database queries?
No, it only scans files and tables at rest during scheduled batch jobs. It does not sit in the path of live user queries or commands.
Why do discovery scanners need high privileges?
The scanner must read every file directory and database table to find hidden records. This requires admins to grant broad read roles and host keys to the tool.