PSEUDONYMIZATION INFRASTRUCTURE

Pseudonymize sensitive data before it reaches systems that do not need the original identifiers.

Replace personal identifiers across text, structured data and files while preserving the surrounding context your workflow depends on.

Email [email protected] about invoice INV-204.

Email[email protected]EMAIL_01

Email EMAIL_01 about invoice INV-204.

One operation across the data you actually use.

[ Replace identifiers inside prose without flattening the text around them. ]

[ Pseudonymize identifier fields while keeping rows and schemas usable. ]

[ Process supported files without manually extracting their sensitive content first. ]

Replace the identifier. Keep everything around it.

Original

User Alice logged in from Munich.
Contact: [email protected]

Pseudonymized

User PERSON_01 logged in from CITY_01.
Contact: EMAIL_01

Pseudonymization belongs before unnecessary exposure.

1. Detect identifiers

→

2. Replace them with pseudonyms

→

3. Continue the workflow

Put it in the data path.

curl https://api.pseudonymize.io/v1/text \
  -H "Authorization: Bearer $PZ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Alice lives in Munich",
    "policy": "default"
  }'

Inspect the handling, not the adjectives.

Data Retention: Zero-retention in-memory processing.
Data Processing: No sub-processors for payload data.