Your SaaS data is full of PII — and it’s flowing into AI.

Teams want to put Slack, Drive, GitHub, and Notion data to work in AI models, testing, and analytics. But that data is saturated with PII, PHI, PCI, and secrets. Send it as-is and you’ve handed an LLM your customers’ personal data with no way to take it back.

How it works

From production to a safe copy — automatically.

1

Connect your source

Point Strac at your data — S3, Azure, or GCP, a common intermediary from your SaaS apps. No agents to install.

2

Detect & pseudonymize

The same ML engine that powers Strac DLP finds every sensitive value and replaces it with a realistic, format-preserving fake.

3

Deliver a safe copy

A copy in the same format lands in your destination — ready for AI, testing, or analytics. Real records never leave your control.

See it in action

Real documents, realistic fakes — format intact.

Every name, address, SSN, account number, and amount is swapped for a consistent, realistic stand-in. The document still looks and behaves like the original — so it’s usable — but no real person is exposed.

Pseudonymized paystub — employee name, address, and pay figures replaced with realistic fakes — Paystub — identity & pay data pseudonymized

Pseudonymized driver's license — name, address, license number replaced with realistic fakes — Driver’s license — PII pseudonymized

Pseudonymized bank statement — account holder and account numbers replaced with realistic fakes — Bank statement — account data pseudonymized

Why Strac

Built for security teams, not just test data.

Consistent everywhere

The same value becomes the same fake across every file, table, and format — joins and relationships stay intact.

Every format

Databases (SQL, Parquet, CSV, DuckDB, SAP HANA), documents (PDF, Word, spreadsheets), JSON/HTML/TXT/EML, source code, and images (OCR).

Your detection engine

The same ML that already governs your sensitive data — names, emails, SSNs, cards, bank accounts, employee/customer IDs, API keys, and more.

Scales to SAP HANA

Incremental processing across 24 snapshots — about 2.15 TB instead of 24 TB (~91% less).

Referential consistency — the same real value maps to the same fake across every table and file — One value → one consistent fake, everywhere

SAP HANA incremental pseudonymization — 2.15 TB processed instead of 24 TB — Incremental SAP HANA processing — ~91% less data moved

Use cases

One safe copy, many uses.

Feed AI safely

RAG, fine-tuning, and analytics on real business data — minus the real identities.

Realistic test data

Dev, staging, and CI that behave like prod, without prod’s risk.

Analytics & BI

Share datasets internally without exposing customers or employees.

Demos & sandboxes

Show real-looking data to prospects and partners — safely.

Pseudonymization is a recognized safeguard.

GDPR explicitly names pseudonymization as a security measure (Article 32). Strac keeps a secure, versioned mapping, logs every field detected and transformed, and never sends real data to a model. Pair it with anonymization when data must be fully non-personal.

GDPR Art. 32HIPAA de-identificationPCI DSSSOC 2Versioned mappingFull audit log

Go deeper: Data Pseudonymization guide · Pseudonymization vs anonymization · SAP HANA at scale

Pseudonymize your data before it reaches an AI model.