Anonymize GLBA Privacy Notice Distribution Lists for Audit – CCPA/HIPAA-compliant de-identification per 15 USC §6801

GLBA (15 USC §6801) requires financial institutions to provide customers with annual privacy notices describing information-sharing practices, generating distribution records that link customer names to their opt-out elections and contact details. anonym.legal pseudonymizes those customer identifiers so compliance auditors can verify distribution completeness and opt-out processing without accessing individually identifiable customer data.

When this applies

Use this workflow when GLBA privacy-notice distribution records and opt-out election logs are reviewed by compliance auditors assessing the completeness and timeliness of the annual notice obligation, or by training teams demonstrating privacy-notice requirements under 15 USC §6801.

  1. Upload the privacy-notice distribution report or opt-out election log to anonym.legal in CSV, PDF, or DOCX format.
  2. The engine identifies customer names, addresses, account numbers, and opt-out election status in the distribution list.
  3. Each customer is assigned a consistent pseudonym applied across the distribution list and any associated opt-out records.
  4. Notice delivery date, delivery method, opt-out election flag, and processing-confirmation reference are preserved in plain text.
  5. A reversible mapping table is encrypted and stored with US data residency.
  6. Export the pseudonymized distribution record for audit or QA review.

What you provide

  • GLBA privacy-notice distribution report (PDF or CSV)
  • Customer opt-out election log
  • Notice delivery confirmation records

Limitations & cautions

  • The tool does not assess whether the privacy notice content meets the Regulation P disclosure requirements implemented under the GLBA framework.
  • Opt-out elections must be honored based on the re-identified customer records; the pseudonymized log is for audit review only and must not be used to process opt-outs.
  • Distribution lists that include joint-account holders require both individuals to be pseudonymized; confirm that both names appear on the list before processing.
  • State-level privacy opt-out obligations may impose additional requirements not addressed by this federal GLBA workflow.

FAQ

Are customer opt-out elections preserved in the pseudonymized distribution list?

Yes. Opt-out election flags and processing-confirmation references are preserved in plain text. Only the customer's name, address, and account number are pseudonymized.

Can this workflow be used to audit the timeliness of annual privacy notice delivery?

Yes. Distribution date, delivery method, and confirmation reference are preserved in the pseudonymized output, enabling auditors to verify timely delivery against the annual notice calendar without accessing customer identities.

Does the workflow cover the initial privacy notice at account opening as well as the annual notice?

Yes. Both the initial privacy notice delivered at account opening and the annual notice thereafter are supported. Upload the applicable distribution record for each notice cycle.

Financial Services Compliance

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.

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.