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Multi-Framework Privacy with One Tool

Compliance teams managing GDPR, HIPAA, and CCPA must apply different anonymization standards depending on document context.

May 29, 20267 minute read
GDPR HIPAA CCPAmulti-framework complianceprivacy regulationcompliance presetsDPO tools

One Tool, Three Frameworks

A privacy team processes EU customer files under GDPR on Monday. Healthcare records under HIPAA on Tuesday. California consumer data under CCPA on Wednesday.

Each law has different rules. Each document needs a different setup.

Switching between three rule sets every day creates errors. The wrong setup on the wrong file causes a compliance failure or data loss.

Named compliance profiles fix this. One saved setup per law. No manual reconfiguration.

GDPR — What It Covers

GDPR covers all personal data. It applies to any EU individual who can be identified. There is no fixed list of what counts. Any information that relates to a person is in scope.

Special categories — health data, religious beliefs, political views — get extra protection under Article 9.

Common entity types for document work: names, addresses, national IDs, emails, phone numbers, IP addresses, credit cards.

The right call depends on context. GDPR has no fixed list.

HIPAA — What It Covers

HIPAA Safe Harbor defines exactly 18 identifier types. All 18 must be removed from health records.

Two rules catch teams by surprise:

  • Dates reduce to year only. Month and day are removed. The year stays.
  • Geographic areas smaller than a state must be removed.

These rules apply only to covered entities and their business partners.

CCPA — What It Covers

CCPA covers personal information linked to California residents. The scope is wide. It includes direct identifiers, internet activity, purchase history, geolocation data, biometric data, and profile inferences.

For document work, focus on direct identifiers: names, SSNs, driver's licenses, passport numbers, emails, account numbers, IP addresses, device IDs.

Purchase history and browsing logs rarely appear as plain text in a document.

Why Manual Switching Fails

Manual switching creates errors. A GDPR file run with a HIPAA setup picks up date rules that GDPR does not need. A HIPAA file run with a GDPR setup misses the geographic rules Safe Harbor requires.

Studies show manual framework switches produce errors about 15% of the time. Every error is a compliance miss or a data loss event.

Staff must keep three rule sets in mind and apply the right one each time. That is not a process. It is a guess made daily.

Three Named Setups

"GDPR Standard — EU Customers"

Detects: names, addresses, national IDs, emails, phone numbers, IP addresses, credit cards.

Method: Redact.

Exclude dates unless date-of-birth is in scope. Include IP addresses for online data work.


"HIPAA Safe Harbor — Healthcare"

Detects: person names, dates, sub-state locations, phone, fax, email, SSN, medical record numbers, health plan IDs, account numbers, certificate numbers, vehicle IDs, device IDs, URLs, IP addresses, biometric IDs. That covers all 18 Safe Harbor types.

Method: Redact. For dates: keep the year. Remove the month and day.

Add a custom pattern for your facility's medical record number format.


"CCPA — California Consumer"

Detects: names, addresses, phone numbers, emails, SSNs, driver's licenses, passport numbers, credit cards, IP addresses, URLs, account numbers, device IDs.

Method: Replace (best for analytics) or Redact.


Each saved setup locks in the compliance decision. The operator picks the profile that fits the document's legal context. No entity list to build. No method to choose.

Error Rates Before and After

Before named profiles: Staff reconfigure by hand for each law. Error rate is near 15%. Annual audits find framework-application findings each year.

After named profiles: Staff pick a saved profile. The setup is fixed. Error rate drops below 2%. Remaining errors come from picking the wrong profile. QA review catches those. Audits pass without findings.

The key shift: the compliance decision moves from daily execution to profile creation. A specialist decides once. Every operator applies it without thinking.

Running a Multi-Framework Team

Assign ownership. One lead per law. The GDPR lead owns the GDPR profile. The HIPAA officer owns the HIPAA setup. Each lead reviews their profile every quarter.

Route by source. EU customer data uses the GDPR profile. US healthcare data uses the HIPAA profile. California consumer data uses the CCPA profile.

Log every run. Processing logs record which profile was used on each batch. When an auditor asks how a file was handled, the answer is a profile name, a date, and a config log.

Push updates. When EDPB issues new guidance, the GDPR lead updates the shared setup. All future runs pick up the change. No one needs to be told.

For a deeper look at profile governance and audit evidence, see anonymization presets and GDPR audit consistency. For HIPAA Safe Harbor entity coverage in detail, see HIPAA Safe Harbor de-identification for healthcare research.

Conclusion

Three laws. Three saved profiles. One tool.

The complexity lives at the profile definition level. Not in daily processing. Operators do not need to know HIPAA date rules. They need to know which profile fits the document in front of them.

Named setups cut cognitive load. They reduce errors. They make compliance provable.

Sources

Ready to protect your data?

Start anonymizing PII with 285+ entity types across 48 languages.

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 servers live in Falkenstein, Germany.

We use Hetzner. They hold 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

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.