anonym.legal

By · Last updated 2026-04-13

Վերադառնալ բլոգինՏեխնիկական

Air-Gapped Gakhtniyutyan. Anon PII Offline

FedRAMP ev ITAR mijavayrnery mek bnin unein` ампуayy xtangum e: GDPR Hor. 4(5)-i ner qo pseudonymization-y ...

April 13, 20269 րոպե կարդալ
air-gapped anonymizationSCIF document processingITAR complianceFedRAMP offline toolsoffline PII detection

Air-Gapped Kanony

Orosh tstitsnerumery internet chunk: Voch թe kаnonакаn — dizayni tarrantsakutyamb:

SCIF (Sensitive Compartmented Information Facility) Faraday-ov patashahet shtap e: Octadriravar signal mi muts, mi durs: ITAR (International Traffic in Arms Regulations) arekum e aparutyan teknik paruncakumery ankhayinum koghmerumery menelum: Ampayyin matakaranrnery ITAR-i martaharman martavatsy chyen: Ayd kаzmakerpumin khagirar "ampayyin SaaS" karavoghakan risk chye, orery petq e karavorel:

Ayd tegherum, ampayyin gorciqnery chyen gortsum: Amboghjovinn:

Gorcik, orumny hetevyal interneti kapoghm e petk, aystegh karog chyar aşxhatel: Gorcik, оry lisensiayi server e kasum, artasyandvats e: Gorcik, оry SCIF-i ners fayl er kaghvum e ampayyin API-i, gortsel karog chye: Ays arakhnabanutyan virahavatumener chyen: Nranq amsaverakan sKzbanadrutyunner en petakan timerineri hamar:

ITAR Depky

Petakan erkumi mek tvayageti tvayagetere ITAR-i arkhami tar kадрайni grasenyakner e kaparum: Ayn petk e haneneri ev ID-nerumery hanenum en zhamanakum pastatghtnerumery kaghvelu: Tstits cer e air-gapped:

Ampayyin lusumkhar chuny: Miak champaghakumny teghayin sherzhatsum en gorciq e: Ayn petq e model nerumery teghayin patrahes: Ayn petq e maq tsagum e arkataghtumneri kaghvelits:

Tauri 2.0-i himqov Desktop havelatsy sа e katum: Tesumits harit, mshakman yntatsqum tstit e gortsum chey tstits cer: SpaCy NER modelnery ev regex pattern-nery bolor katarum en teghayin CPU-i vra: Ardumky mnasum e sarkum, minchev ogtaherutyan hartsum exportayelu:

Inchu E Karevelyuynutyan Masnagitutyun Kangnumy

Gakhtnayakan ashxhatanutyan ev pseudonymizatsia karevelyuynutyan petky e: Time ery irantsnum en irakakan anunnery kodner: Pahpanum en ashkhatanqy ogtagortzeli: Pahum en irakakan indentiteterumery:

GDPR-i 4(5)-rd hodvatsn e pseudonymizatsiay kanоnakanam gaghapari koghmic hastatvogh gaghaparan mi prak: Ayn korcnenum e riskumery: Pseudonymized grasenyaknery kirum en avin iravakan patareskutyunnery — ete tochakayutyan tonoy pahnvats e tvalabaseits:

IAPP hetazotutyan (2024) nkht e, vor gorciqneri miyayn 23%-n ishkhun karevelyuynutyan ajakatutyun e: Metsameraserutyuny miastin kоdavoгutyun kаm ambagjin pchogh e: Yerb grasenyaky gortzanum e, linin ambagjin korcats:

Orosh petakan timerinere karkaravacum en irenq ashkhatanqn khorhumnerov: Mek time n psevdonymized faylery stanum e: Nranq katarum en verclutyumery: Erkord time n tochakayutyan tonumery pahu: Nranq nordan en gnnahatkel grasenyaknery, erkb kanonumery pahanjum e: Ayd bakhum dizaynum e arakhin karuputyun ampakhmatsvortsakan chixakerpatumakan gaghaparan hamar:

Zero-knowledge modelumы ev mek qayl e gnum: Tochakayutyunumny steghtsum e vortahi sherzhatsum en: Ayn yerbeq durs chyar kаghvel: Ete matakaranutyan patgamumery karаvaran, nranq tochakayutyunumny chkaron handznum: Nranq yerbek chuneyiin: Ayd pataskhanum e chkanchuman kargin kanonnerin bazmakazmak klassifikatsyad gakhtnayakan mijavayrneri aşxhаtanqumum:

EDPB Token Bakhmanumny

EDPB Guidelines 05/2022-umny asumny, vor pseudonymizatsiai tokenum petq e bakhmanumny pahnvatz: Ayn petq e chkakhvumny pseudonymized graso enyaknerum yev petq e kloгvet verjadzerneri, orоnq karqum chey artavaytutyan vortahi chem paymanav tochak ev ardzangr grasenyaknery miasnasatem:

Ereq ban miasnum en katrelu or kanonumny:

  • Token steghtsum e vortahi sherzhat — yerbeq durs chye kagh
  • Ambagjin mshakum e teghayin — vochinch е lqum air-gapped tertskarum
  • Ardumky ev tokenumы artahanvum e bakhmanumov — erku bakhmanumnal fayl, erku bakhmanumnal chapaghak

Ayd dizaynum katarum e EDPB kanoni ev air-gap sKzbanadrutyunan patanjnerumery miastin:

Ambagjin patkeracman hamar, mer anvangutyan aknerky cutsarum e, inchpes teghayin mshakum krchatele er kord-koghmy chaxutyunumery: Mer hamapataskhanutyunan ughekovery ery GDPR transferi kanonery: Tesnir mer FAQ-y kazmakerputyan ognutyan hamar:

anonym.legal Desktop havelatsy PII-i ambagjin haytnaberutyan e katarum teghayin sherzhat: Tesumits harit internet petk chye: Ayn ashkhatum e Windows, macOS ev Linux: Endalvats NLP modelnery ajakatsum en 24 lezuneri:

Tharmaкucvac 2026-i Hamar

Aghbyurner

Հոդվածներ, որոնք կապված են

Տեխնիկական

Cross-Platform PII: Mac, Linux, and Windows

Privacy officers on Mac, legal on Windows, data engineers on Linux — all processing the same data with different tools. Here's why OS-agnostic detection.

Տեխնիկական

Cross-Application PII: Word, Chrome, and AI

Customer data flows from browser research to Word drafts to Claude prompts. Each context switch is a potential leakage point.

Տեխնիկական

GDPR in App Logs: JSON PII Compliance

Application logs contain customer email addresses, IPs, and account numbers that GDPR Article 5(1)(e) requires be managed.

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.