See where sensitive data actually lives, then decide what to do with it.
VestraData connects to databases, file stores, and cloud buckets, then shows which fields need review. Teams can document findings, apply de-identification rules, and generate audit evidence without moving data into a third-party service.
Detects obfuscated column names. A field called "usr_eml" is flagged as personal email, not ignored.
Multi-pass adaptive sampling avoids full table reads during discovery.
Supports masking, redaction, hashing, format-preserving transforms, and synthetic replacement.
Works across structured and unstructured sources in one review model.
Give engineering and analytics teams useful data without widening production exposure.
Synthetic data only works if it still behaves like the original system. VestraData preserves enough of the statistical shape and table relationships for testing, analytics, and model development without handing teams live customer records.
Preserves column distributions and cross-table relationships.
Maintains foreign key integrity so the data still joins correctly.
Exports to staging databases, object storage, and ML pipelines.
Supports scheduled refreshes instead of one-off manual copies.
Documents leave the organisation clean, or they do not leave.
Connect VestraData to a document store and it prepares governed copies in the background. When someone needs to share a file with a partner or upload it to an external tool, the safer version is already available.
Monitors SharePoint, Google Drive, Dropbox, and S3 for new or changed files.
Creates governed copies indexed by file hash for fast retrieval.
Keeps mapping and review context for audit purposes.
Reduces manual review work at the point of sharing.
Personal data should be reviewed before it reaches any external AI tool.
Teams are already using ChatGPT, Claude, and Copilot. VestraShield adds a control point at the browser so prompts and uploads can be warned on, blocked, or transformed before they leave the user's machine.
Applies policy to prompts and uploaded documents.
Can warn, block, or transform content before submission.
Runs with the same detection logic as the rest of the platform.
Keeps AI controls aligned with existing privacy policy instead of creating a parallel process.
