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Վերադառնալ բլոգինGDPR & Համապատասխանություն

DPO-ի stawchakh GDPR 28-rd hodvatsov. Anonymous Gorcikneri Vetting

GDPR-ի 35-rd hodvatsы petajum e DPIA barzrakharj mshakutyan hamar: ISO 27001 sertifikacиany krtsel e angitak hetaqnnery lracnely 73%-ov:

May 9, 20269 րոպե կարդալ
DPO GDPR vendor assessmentGDPR Article 28 checklistDPIA anonymization toolISO 27001 procurementdata processor evaluation

Inchu DPO-neryn pits e vetagnel anananuncman gorciknery

GDPR-y petajum e Tvealneri Pashtpanutyan Harashutyun Gtahatum (DPIA) barzrakharj ashkhati hamar: PII-i lratsatsum barzrakharj e: Anananuncman gorcinke tvealneri mshakits e: Ayv enkalnum e mshakitsnery kanonekey pakhov: Petk e getit norits meke gnahatelen harev:

Erku ban pakhavory e: Mshakitsnery petk e tain "bacavakan yerashxtikutyunner" angamutyan hamar: Bolor mshakuty petk e karavarutyun linerа gorvayk pashavery: Vorpes DPO, karogagriry gorcikneri angamutyan hakakaghnapahnery, yeta enos-procesornery, ortegh e paymanatru yeta faylerimater, inchpisi e veramyuteri en veraberum ev tvealneri mshakutyan pashavany (DPA):

ISO 27001 sertifikacian katsum e ashkhaty: BSI-y gtnarel e, vor sertifikacvats firmaner nvaltsrum en angamutyan hetaqnnery lracnely 73%-ov: Gartner-y gtnarel e, vor ISO 27001 petajvum e Fortune 500-i khorhanerd 78%-ум: Sertifikacvats gorcinke karo e nshvum sertifikati vra: Pits chhe yuraqanchiur hakakaghnapahn dzeragrel: Sertifikacvats gorciknery aveliy manral patashkhar en yetaqndrel:

Tes mer hamapataskhanatsutyan bnagiry ev angamutyan kanchy` kalognanal, inchpisi enq havasarakrum ayd kannonner:

Yot ban yetaqndreluys

Ogtagortsir ayd cegh anadzdzan anananuncman gortsiny ev maternaby hamar:

1. Tvealneri mshakutyan pashavan: Kа ar GDPR-hamapataskhan DPA? Ayv petk e enkalelе. mshakuty miain dzez karchery karogel, handgyutsutyan parartutyunner, angamutyan qaylerum, undenp-mshakitsnery karavarel, iravunqneri harrumnerim neroq, faylerik mshakuty ev handanely ev veradzernakhabi iravunqnery:

2. Angamutyan noterumnery: Angamutyan qaylerum gotsvats en? ISO 27001 sertifikacvats maternabner karogum en nshum irenk sertifikati vra ev irenk Statement of Applicability-y: Ayd bavarakarachnum e pahanjanky:

3. Undenp-mshakitsneri tsank: Gorcinke andenp-mshakitsnery karogh e? Kochevu'n Nrank?: Yayadz vokhe pitan ev tzanucagrutyun pitas: Amenameyn hostingi, CDN-nery ev OCR-y hamarvum: Batzakach anunner handakavan bats en:

4. Faylerik panteli vayry: Dzez tvealnery pantvum en EM-um? EM-i hostingy EM-based firmaneriv ankal ashkel e: Zrodyayin-gitutyun gorciknery nayev kayakanum chen — faylerik ays device-ic arts chhen yerkokhtum: ԱՄՆ-based maternabneryn pits en Standard Contractual Clauses (SCCs):

5. Khotqagrman ceghekutyun: Vor areceli aragnutyamb maternaby dzez teghekatvu e khotqagrman masin? Orenqy petajum e dzez karogakanutyanpah 72 jamik vnaskarich aghbertum: Dzez maternaby arach petk e teyekatvu dzez: Hastaputy, vor DPA-y hastatec ayd:

6. Maternabi DPIA: Arve ar maternaby irenk DPIA e kantsrats? Karogh es karchatsel? DPIA-i batsaknery nshanum e barts dzez sephakan noterumnerum: Ayd hertakan harcakum e:

7. Handanely ev krepavorely: Karogh e ar maternaby verahasknel handanely ev krepavorely harrumner? Zrodyayin-gitutyunin gorciknery faylerik misht chen zahkum, usti handanely karo e kayelum chen: DPIA-y petk e ayd asovoum e:

Lav maternaby dzez ta chors niv: ISO 27001 sertifikat, EM-i hostingi apatsuyts, irenk DPIA ev storyagir DPA: Ayd chors niver ltsnum en dzez DPIA-i amanach bartz: Dzez karoghakann kayekum e bavarakarcvum:

Karse mer DPO FAQ maternabi vetting-i shrjanapacky masis hamakar aclum hamareri hamar:

Aghbyurner

Հոդվածներ, որոնք կապված են

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Japan My Number: Verhoeff & APPI

63% of generic tools fail My Number detection in Japanese documents. My Number uses Verhoeff algorithm — the most complex national ID checksum in Asia.

GDPR & Համապատասխանություն

HDPA Greece: AFM & AMKA Detection

Greek AFM detected with 52% accuracy by generic tools. HDPA issued 89 decisions in 2024 — up 162% from 2022. Tourism and maritime sectors face distinct.

GDPR & Համապատասխանություն

NAIH Hungary: TAJ-Szám and Adóazonosító Jel

Hungarian NER accuracy is 67% vs. EU average 82% — NAIH's 2024 assessment. TAJ-szám weighted checksum and adóazonosító jel detection gaps.

Պատրաստ եք պաշտպանելու ձեր տվյալները?

Սկսեք PII անանոնիմացնել 285+ կազմակերպության տեսակներով 48 լեզուներով:

Սկսեք անվճար փորձաշրջանԴիտել առանձնահատկությունները

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.