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Verarakvats gaxtutyun. ML Preset-ner

ML usucman dataneri ananunacoocmman petq e hamazeq u verarakveli linel: Yete A u B datagi-ginern kiravarucnum en tarberacan andzin tipery, usucman zhamanakaxmbagrutyunery en:

June 4, 20266 րոպե կարդալ
ML training datareproducible privacyGDPR AI ActCNIL enforcementdata science compliance

Verarakvats Gaxtutyun. Inca en ML Xumbnery Petaguml Preset-ner, Oc Miayni Fastaratsumner

DPO-n hastvatsrets e ananunacoocman nakhagitsy: Kapcvum e chors ishxanats: Anunner, el. postere, helefonanner u tsnnundiatsneri tarkakergun: Kazmakerpcum-n Replace e: Nakhagitsy cor kaparan u bnakvum e compliance-i wiki-um:

Tasnerku dataginer-n kardacum en ayn` kazkutyan nistum: Nranqic yuraqanchyurn ir gorciky kazmavorum e ink andznakazmak: Merumnery avelagelum en azgayin ID-ner: Meruq-nery IP hashvacner en avelagelum: Meruq-nery anbartavar en Redact: Yerec amis heto, zhamanakaxmbagrutyunery hamazeq chen:

CNIL-n mec AI firmaner verahastarets 2024-in: Xndiry: Andznayin detalteri anupatakami kirarutyunn model zhamanakaxmbagrutyunneri mej: Nranq chi harcrel, ardu ananunacoocmman gorcel e: Nranq harcrel, inchpes hamazeq e kazmakerpcvats:

Fastaratsumner anhrashest en: Nranq bavar chi en: Bzhishutyunn preset-n e:

Inca en ML Model Zhamanakaxmbagrutyunnery Petagumel Ir Sephalik Kazmavorutyun

Model zhamanakaxmbagrutyunner karucutyunn unecnum e benzeal karedyutyan: Sharakar fastaratsumyi ananunacoocmann ayanq chi kisu:

Replace, oc Redact: Model-nery, vornq usucvum en tekstayin vra, vur anunner darnum en [REDACTED], usucvum en ayd toxenn orpes anvan-dires nshich: Ays vnaharvum e model-n: Replace-n plochem e "John Smith"-n "David Chen"-ov: Model-n tesnum e iravakan anvaneri dzevachapner: Chi tesnum e dimi toxen:

Nuy kazmakerpcum bolor grasenyakneri hamar: Zhamanakaxmbargrutyun, vur anunnery replace en elen 70%-um, baic [REDACTED] en mnum 30%-um, xarnararum e xarnararakan nshan: Amenayin grasenyakn petq e andnum e nuy qaydery mej:

Nuy andzin tiperny: Yete zhamanakaxmbarutyunn parhunanum e bazhkanutyan detalnery, anunnerny hatucelu en tsnnundiatsnery, baic mnum en mots zhamanakaxmbargrutyan grasenyaknerum arjunq e aracnum: Bolor tasnerku dataginer-n petq e hatucen nuy tiperny:

Lratsuzich chi hatucnel: Mshakman tarkanergun-nery, vornq timestampner en - oc tsnnundiatsneri tarkanergun - hatucnelu en nvazelyum e zhamanakaxmbagrutyan vich het chi lratzucelu: Kazvats preset-n asum e shetamit vorn e hatucelu:

Krknaneli arqunq: Yete zhamanakaxmbagrutyan petq e naynov gortcarkutyun - aseink, erbayin bacrakan andzin tip gtvelic heto - preset-n nuy arquncky e tarilutyunneri amenayin angam: Hatakanvats konfiguratsianer ays e aracum e:

Tasnerku Datagini Xndiry

Finantech ML xumb Europayi mej kiravarucnum e hachakhorde-i murnurneric zhamanakaxmbagrutyanery: DPO-n hastvatsrets e npatakn - kexbakanutyunyi hraparakanutyan - mek kantakagrov. bolor hachakhorde-i anunner, el. postere, helefonanner u varchakanutyunyan ID-ner petq e replace elen naxqan model-i ashkhatanqn skselu:

Bex preset-ner:

  • Andzn 1-n hatucum e anunner, el. postere u helefonanner - baic bacer e varchakanutyunyan ID-nery
  • Andzn 2-n nerkayacnum e varchakanutyunyan ID-ner, baic kiravarucum e Redact, oc Replace
  • Andzn 3-n anushem e nakhagits fastaratsmany shetamit
  • Andzn-nery 4-12-n tarbervum en

Miadzulvats zhamanakaxmbagrutyan maseampory anstanarc e u maseampory voratsvats e ashkhatanqov: DPO-n vkaytel chi karac ayn:

DPO-i kastvats preset-ov:

  • DPO-n kazmavorel e "ML Dev - Kexbakanutyunyan Hraparakanutyan" shetamit andzin tiperin u Replace kazmakerpcmamb
  • Preset-n gnacel e bolor tasnerku andzineri mot mek kantakagrov. Kiravaretsek ays bolor zhamanakaxmbagrutyan ashkhatanqi hamar
  • Oc okines karol e tarberacel preset-n bex DPO-i hastvatatsutyunyi

Amenayin andzn aris nuy arquncky e: Miadzulvats zhamanakaxmbagrutyan hamazeq e: Amenekin AI vericutyunn anvahank e andanum bex gtumneri: Ankumani taresty yerec gtumner uner zhamanakaxmbagrutyan anhamazeq ashkhatankutsits:

GDPR u AI Kanony

Tarbetsvel 2026-in

EU AI Kanony liorin oushal amenayin ogustein 2024-in: Ayn avelagelum e kantakagnery AI hamakargneri hamar, vornq kiravarucnum en andznayin detalnery model-i ashkhatanqneri hamar: Barz-riskayni AI hamakargnery petq en fastaratsen irenq zhamanakaxmbagrutyanery, ner arad, anunacoocmutyunn kazmakerpcvats e:

GDPR Hod 5(1)(b)-n - npatakakir shertayin kantakagi kantakagy - argelum e andznayin detalneri kirarutyunn bex parzkakan iravakan hasnut'yan: CNIL-i 2024 deptqery kedradzum en ayd bachi. mek tsarautyunyi hamar kaxvats detalnery, model-i ashkhatanqnerum kirarutyamb, bex vaverakavats hasnutyan kam ananunacoocman:

Preset-nery krakanarelu en erku kantakagneri zaruts:

  • Preset-i anun u konfiguratsian. fastaratsvats mekodny
  • Mshakman vkayagirery. mekodny kiraravvats linel e apacuyt
  • DPO-i hastvatatsutyunn. kazdagrvats hasnutyan vra:

Ays kazmavorutyunn erku kantakagnerum el petagumed vkayagirkany e: Hod 10 paryavirtnutyunner mej, tesek EU AI Act usucman dataneri usuccuyvani:

Preset Kazmavorutyun NLP Model Zhamanakaxmbagrutyaneri Hamar

Tipery, vornq kapcvum en bolor NLP model zhamanakaxmbagrutyannerum:

  • PERSON - Replace nman anunnerin het
  • EMAIL_ADDRESS - Replace sinthetik hashvarcnerin het
  • PHONE_NUMBER - Replace sinthetik hamarnerin het
  • CREDIT_CARD / IBAN - Replace kam Redact
  • LOCATION - Replace nman verenerin het, yete verengin kensakal e; Redact, yete oc
  • DATE_OF_BIRTH - Redact; taryakni khmbavorum harcaki petagumal e

Tipery, vornq harcaki zahvum en baxkum:

  • Sharakar tarkanergun - time-stamper-n ev orake temporal model-neri hamar
  • Kazmakerpcman anunner - ognum en anvanvats-andzinyin model-neri
  • URL-ner - ognum en kapakic u haghordakic model-neri

ML avakn u DPO-n or kantakagnery kazmavorel en pahvats preset-um: Xumbi andam-ner kazmakerpcum en ayn: Nranq konfiguratsiayan yntanelutyunner chi katarutyun:

Preset-nery orpes Khazoghakan Hiscakutyun

Naxqan preset-nery: Uight andatat konfiguratsia bnakvum er yerek dataginer-i glkhnerum: Nranq ashkhatets in compliance vejercutyamb: Erkon Q3-um arjakan gurjetsav: Gitutyunn irenq chen gnacel:

Preset-neric heto: Konfiguratsiann bnakvum e "ML Dev - Hachakhorde-i Katrumnery v2.1"-um: Tarberakti vkayagir-n cursavum e, yerb e kazmavorvats, ov e hastvatsrets, u inchpes e tarbervum v2.0-its: Nor xumbi andam-nery kiravarucum en preset-n u stananum en bardy namoy inormatsian:

Tarberak 2.1-n avelagets IBAN haytnabervelutyan vejercum-n gortvecuyc gtav e anbn bacrakan: Tarberak 2.0-n hastutvats e 2025 fevrali: Vkayagirn lriv e:

Mshakman vkayagirery u DPO vejercutyunyi hoshqery, tesek GDPR ML usucman dataneri ananunacoocman usuccuyvani:

Preset-ner enddema CNIL Dzevachap

CNIL-i 2024 AI deptqery mek sharcik dzevachap en aratsum: Nranq harcnum en oc miayni, te inche e hatucvats, ayl inchpes e karavaragvats: Kaznvats preset` DPO-i hastvatatsutyunyi vkayagrov u mshakman vkayagirnerov anmijapespot e pataskhanvum:

Hatakanvats konfiguratsian` oc: Nuy bache goyutyun uney ayl EU DPA deptqerum, vornq hetevum en CNIL tarqn: CNIL-i AI mots-i veraberyal, tesek CNIL GDPR AI compliance usuccuyvani:

Yezelabach

Fastaratsumnery xmbi andam-neri kargivnery asell en, te inca katarucnen: Preset-nery asanacnum en - u karang dardzenum e - nuy kerepov katarucnel amenayin angam:

ML model zhamanakaxmbagrutyaneri hamar, hamazeqnutyunn kanonirc u texnikakan anhrashesht e: Preset-n erkoum el mijamtov bavarakanim e:

DPA-nery, vornq nayum en AI kanoner, uzum en hamaynakan ananunacoocman apacuyt: Preset-n, kazmakerpcvats nuy kerepov bolor zhamanakaxmbagrutyan ashkhatanqnerum, lravagnin apacuyt e, vor karan tal:

Ashxarhnery

Պատրաստ եք պաշտպանելու ձեր տվյալները?

Սկսեք PII անանոնիմացնել 285+ կազմակերպության տեսակներով 48 լեզուներով:

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.