Automated PII Masking
Sending raw PII to an AI agent, a third-party integration, or a developer's local machine is a compliance exposure, and column-level tagging doesn't scale as your schema evolves. Safe Boundary detects and masks PII in query results automatically, at the proxy layer, using AI classification that requires no manual annotation.
PII leaves the database the moment you query it
Once a row crosses the wire, it lands in application logs, AI agent prompts, error trackers, support tickets, and developer laptops. Every one of those surfaces becomes part of your regulated perimeter, and most of them were never designed to hold PHI, payment data, or personal identifiers. Safe Boundary fixes the problem at the source by masking sensitive values inside the result stream, so the data on the wire is already safe by the time anything downstream sees it.
Detects PII without column tagging
Email addresses, names, phone numbers, credit card numbers, and SSNs are identified in query results by an AI classification model running in the proxy. You do not need to tag columns in your schema or maintain a data dictionary.
No migrations, no decorators, no per-column tagging. You describe categories in policy; Safe Boundary finds them in data. New columns and new tables are protected the moment they ship.
Name and SSN detected
Email address inline
Category and confidence
Masks data before it leaves the database layer
Masking happens in the proxy response path, before query results reach the application or AI agent. The underlying data in the database is never touched. Applications see masked values; your database retains the original records.
Choose deterministic tokens to keep joins working, format-preserving masks to keep UIs happy, or hard redaction for the strictest categories. Everything is driven by policy, evaluated per query, and logged for audit.
Replaced at the proxy layer
Deterministic tokens
Reversible for reviewers
Scoped per identity and policy
Masking rules are applied per user identity, agent identity, or role. A privileged analyst can receive unmasked results under a specific policy while all other identities see masked output, no schema changes required to manage this distinction.
Human identity aware
Masks apply differently to analysts, developers, and customer support, without rewriting the app.
Agent-aware policies
Each AI agent and automation carries its own identity and its own masking contract.
Per-role redaction
Stack policies by role, environment, and data category, evaluated for every single query.
Audit-ready classification tags
Every masking decision ships with structured evidence, category, column, confidence, identity, and policy. Auditors get a full, queryable trail; engineers get a log that explains exactly why a value was redacted.
Every masked field carries a classification tag, a confidence score, and a pointer to the policy that fired. That audit payload is exposed via API and streamed to your logging pipeline, ready for compliance review or SOC investigations.
Category, column, confidence, policy, and outcome are captured for each masked field.
Stream classification events to Splunk, Datadog, or S3 for long-term retention.
Tags map cleanly to regulatory categories so audit evidence writes itself.
Preview how a new policy would affect yesterday’s traffic before you turn it on.
Enable PII masking on your database, no schema annotations, no code changes.
Free for 1 database. No credit. No Time limit. Full AI SQL Injection prevention.