Data Privacy Insights
Expert articles on AI security, GDPR compliance, healthcare data protection, and PII anonymization best practices.
All Articles
Real-Time PII Prevention Saves $2.2M
IBM found a $2.2M cost difference between prevention and detection. Here's the math that makes real-time PII interception non-optional for security teams.
GDPR Art. 32: AI Tools PII Monitoring
Enterprise compliance teams need quantitative evidence of AI tool PII controls. Network DLP misses browser AI interactions.
Real-Time PII Prevention for AI Data Leaks
When an employee types a customer name into ChatGPT, the data leaves organizational control in real-time. Post-hoc DLP cannot un-ring this bell.
Self-Hosted PII Fails Compliance Audits
spaCy 3.4.4 produces different NER results than spaCy 3.5.1. Financial services firm discovers 3% of documents were differently anonymized in staging vs.
Presidio: 3-Week Setup vs Managed PII
Microsoft Presidio has thousands of GitHub stars and hundreds of open issues. Setup complexity, PySpark integration overhead, and Python dependency.
6 Weeks to 3 Days: Managed PII Setup
Healthcare SaaS teams spend 6 weeks on self-hosted Presidio production deployment before switching to managed API. The managed API replaces the deployment.
Presidio Misses 220+ GDPR Entities
Presidio ships with ~40 default entity recognizers focused on US identifiers. European organizations need IBAN, Codice Fiscale.
Free PII Detection Costs €13K/Year
Self-hosting Presidio requires 40-80 hours initial setup and 5-10 hours/month ongoing maintenance. At €100/hour engineering rates, that's €13,200+.
Presidio 22.7% Precision Problem
A 2024 benchmark found Presidio's person name recognizer achieves 22.7% precision in business documents — meaning 77.3% of detections are false positives.
Cut Privacy Training: Weeks to Hours
Privacy tool onboarding typically takes 2-4 weeks, with a 22% first-week configuration error rate. Shareable presets reduce training to 1 day and.
MSPs: Standardize Anonymization
MSPs and compliance consultants serving multiple client organizations cannot manually reconfigure PII tools per client at scale.
Configuration Drift: A Hidden GDPR Risk
Analyst A replaces names with pseudonyms. Analyst B blacks them out. Your GDPR audit finds both in the same dataset. Configuration drift — where team.
Reproducible Privacy: ML Presets
ML training data anonymization must be consistent and reproducible. If data scientists A and B apply different entity types, training datasets are.
Multi-Framework Privacy with One Tool
Compliance teams managing GDPR, HIPAA, and CCPA must apply different anonymization standards depending on document context.
Anonymization Presets End Inconsistency
When 8 paralegals independently configure PII anonymization, inconsistency is inevitable. GDPR auditors look for systematic, consistent application of.
HIPAA MRN Detection Without a Regex PhD
Every hospital's MRN format is different. Memorial uses MRN:XXXXXXX, St. Mary's uses PT-YYYYY, University Hospital uses UHN-XXXXXXXXXX.
Legal PII: Privilege Detection
Case reference numbers, bar admission numbers, court docket numbers, and client matter IDs are legally sensitive identifiers that standard PII tools miss.
GDPR Support AI: Custom Identifiers
Customer support AI receives customer messages with names, emails, AND order IDs. Standard PII tools strip email addresses but leave order IDs intact.
EU National IDs Your PII Tool Misses
Germany's Steueridentifikationsnummer, France's Numéro fiscal, Italy's Codice Fiscale, Spain's NIF/NIE — US-focused PII tools detect SSNs but miss most.
Beyond SSNs: Internal ID Anonymization
Every organization has internal identifiers — employee IDs, account numbers, order IDs — that are personally identifiable in context but missed by.
HIPAA: Hospital-Specific MRN Detection
HIPAA Safe Harbor requires removing medical record numbers — but MRN formats are not standardized. Epic, Cerner, and Meditech all use different formats.
GDPR Pipeline: Anonymize Before Storage
dbt column tags are not GDPR compliance. Raw customer data hits your Snowflake warehouse unmasked before tag-based policies apply.
FOIA: Redaction from Weeks to Hours
The federal government spent an estimated $500M on FOIA processing in 2024, mostly manual redaction. ARPA-H explicitly sought AI redaction software to.
GDPR ML Training Data Anonymization
GDPR restricts using personal data for ML training beyond its original collection purpose. Data scientists relying on ad-hoc Python scripts create.
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About this page
We update this page when our platform or the law changes.
Read our founder note for how we work.
Each change shows up in the timestamp at the top.
Related reading
We follow these rules
- GDPR (EU 2016/679).
- ISO/IEC 27001:2022.
- NIS2 (EU 2022/2555).
- HIPAA safe harbor under 45 CFR § 164.514(b)(2).
Our promise
We do not sell your data.
We do not train models on your text.
We store your files in Germany.
You can delete your account at any time.
You own your work.
Where we run
Our company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.
Hetzner holds ISO 27001 certification.
All data stays in the EU.
Backups run every day.
Need help?
Email support@anonym.legal.
We reply within one business day.
How we test
We run a full check suite on every release.
Each surface gets its own sweep script and report.
Human reviewers spot-check the output each week.
We track recall and precision on a labelled set.
Bad runs block the deploy.
What we never do
- We never sell your information to third parties.
- We never train models on what you upload.
- We never keep your work after you delete it.
- We never share keys with any outside firm.
- We never run ads inside the product.
Plans in plain words
We sell credits, not seats.
One credit covers one short job.
Long jobs use a few credits each.
You can top up at any time.
Unused credits roll over each month.
Read the plans page for current rates.
Who built this
A small team of engineers and lawyers built this.
We ship from Europe and work in the open.
Our founder note spells out why we started.
Where to start
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
How the parts fit
A browser add-on cleans text inside Chrome.
A Word plug-in handles drafts in Office.
A small desktop tool works on whole folders.
An agent protocol link feeds large models safely.
All four share one core engine and one rule set.
Words from our team
We started this work after a lunch about cookies.
One friend kept getting odd ads on her phone.
We asked why a court file leaked through a draft.
We sketched the first build on a napkin that week.
By month three we had a tiny demo for a friend.
She used it on her first case the next day.
Common questions we hear
Can the tool read scanned PDFs? Yes, with OCR.
Does it work on long files? Yes, in small chunks.
Can I roll my own rule set? Yes, save it as a preset.
Does it run offline? The desktop build runs offline.
Do you keep my files? No, the cloud build wipes after each run.
Will it learn from my work? No, we never train on inputs.
A short tour of the workflow
Upload a file or paste a snippet of prose.
Pick the entities you want gone from the draft.
Choose a method: replace, mask, hash, encrypt, or redact.
Press run and watch the side panel show each hit.
Skim the result and tweak any rule that misfired.
Save the cleaned file or send it to a teammate.