GDPR-Compliant Anonymization for Text Data
Automatically detect and mask personal data. GDPR-compliant text analysis with automatic PII detection.
One name in 10,000 texts
Finding personal data in free-text responses is like finding a needle in a haystack. One overlooked name, one forgotten email address – and you have a GDPR problem. Manual review is neither practical nor reliable.
- GDPR fines up to €20M or 4% of annual revenue
- Reputation damage from data breaches
- Manual review misses an average of 15% of PII
See Anonymization in Action
Hi, I'm John Smith (john.smith@company.com). Please call me back at 555-123-4567. My address is 123 Main Street, New York, NY 10001.
Hi, I'm [NAME] ([EMAIL]). Please call me back at [PHONE]. My address is [ADDRESS].
Meets the Highest Standards
Automatic PII Detection
Names
John Doe → [NAME]Emails
john@company.com → [EMAIL]Phone
+1 555 1234567 → [PHONE]Addresses
123 Main St → [ADDRESS]IBAN
DE89 3704... → [IBAN]Custom
Define your own rulesPrivacy at the Highest Level
PII Detection
Automatic detection of names, emails, phone numbers, addresses and more.
Flexible Masking
Choose between replacement, pseudonymization or complete removal.
Configurable Rules
Define your own detection rules and exceptions for your use case.
Audit Trail
Complete documentation of all anonymization steps for compliance.
Choose the right approach
Depending on your requirements, you can fully anonymize, consistently pseudonymize, or generalize for maximum analyzability.
Redaction
Complete removal – the text is replaced by [REMOVED] or similar placeholders.
John Smith → [NAME]Ideal für: Maximum security when the original value doesn't matter
Pseudonymization
Consistent replacement – the same person always gets the same placeholder.
John Smith → Person_A (everywhere in the text)Ideal für: When you need to preserve relationships between people
Generalization
Replacement with general categories – context is maximally preserved.
john@company.com → [EMAIL]Ideal für: When context matters, but not the exact data
GDPR Compliant by Design
Anonymize data before analysis – this way you can process sensitive text data in a GDPR-compliant manner.
- Processing before storage
- Documented processes
- Audit trail
- Configurable rules
Anonymization vs. Pseudonymization
GDPR clearly distinguishes between anonymization and pseudonymization – with different legal consequences.
Anonymization
Personal data is irrevocably removed. Re-identification is not possible.
John Smith → [NAME]Pseudonymization
Data is replaced with a consistent placeholder. Re-identification would be possible with the key.
John Smith → Person_A (consistently throughout dataset)For most analysis purposes, we recommend full anonymization. Pseudonymization is useful when you need to analyze relationships between individuals.
Why Anonymization Is Mandatory for Text Analysis
Open text data almost always contains personal information – names in complaints, email addresses in feedback, location data in reviews. Without anonymization, this data cannot be analyzed or shared with third parties under GDPR.
- Art. 6 GDPR: No processing of personal data without a legal basis
- Art. 25 GDPR: Data protection by design and by default
- Art. 32 GDPR: Appropriate technical protection measures required
- Art. 83 GDPR: Fines up to €20 million for violations
Especially Important For
Frequently asked questions
Combine with other features
See your open responses as structure – not as a wall of text
Start directly with your own data or validate your use case with guidance – including stakeholder assurance.