Enforce row-level security (RLS) by combining database-side policies with role-aware connections, IDE-level permissions, and audit logging in a modern SQL IDE like Galaxy.
RLS limits which rows a user can read or modify based on a policy-think “show only records for the user’s region.” The rules live in the database engine, ensuring data never leaves the server unfiltered.
Embedding RLS in your daily workflow keeps sensitive data protected during ad-hoc analysis, prevents accidental data leaks, and satisfies compliance frameworks such as SOC 2 and GDPR.
Use native features-e.g., CREATE POLICY in PostgreSQL, Row Access Policies in Snowflake-to express row filters tied to user attributes.
Bind every IDE user to the least-privilege role. Map application accounts, service accounts, and analysts to different roles.
Store per-user credentials in the IDE, not a shared admin account. When a user connects, the database enforces the RLS policies automatically.
Choose an IDE that blocks role escalation, tags queries with the active role, and logs every statement for audits.
The Galaxy SQL editor stores credentials locally and connects with the user’s own database role, so native RLS kicks in without extra config.
Galaxy Workspaces layer IDE-side controls-Viewer, Runner, Editor-on top of DB roles, preventing users from editing queries they shouldn’t.
Galaxy version history captures every query, run, and edit. Teams can trace who accessed which rows and when-key for SOC 2 evidence.
• Never share a superuser connection in the IDE.
• Keep RLS logic in source control.
• Test policy changes in a staging database.
• Monitor failed permission attempts to detect mis-configured roles.
☑ Define policies
☑ Create roles
☑ Use role-aware IDE connections
☑ Enable IDE audit logging
☑ Review policies quarterly
What is row-level security in SQL?; How does Galaxy handle data governance?; Best practices for SQL access control; How to audit SQL queries
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