anonym.legal

By · Last updated 2026-04-11

Վերադառնալ բլոգինԱռողջապահություն

50 Հազ. Կլինիկական Գրառում. Տեղային Խmbakayin Mshakum

2026 թ. փետրվարի SDNY որոշումն արձանագրեց, որ AI-ի կողмik mshakvac փաsатաghterra կoram en korizum hrazhdakan-hvatakarakanutyan imunitetit, ete anvazhen mshakman araджin ananunacvac chen linum:

April 11, 20268 րոպե կարդալ
batch PHI de-identificationclinical notes processingHIPAA local processingresearch dataset complianceIRB requirements

50,000 Կlinikalakan Graroum Teghayinum. HIPAA Ughekуcoytь

Hetazotakan time nery, oronq karik uni masghin mets note arxivner de-nuynakacel, baxarkkum en ynthanur bac: Ампуayyin gortiqnere herapshakel en bazmakshelov: Kanonner metsameraserutyan shurjy patayum en teghayin ashkhatanutyan nakhatarakutyun: Teghayin khaknakayayin ashkhatanqy pataskhan e:

Ays oughekoytzn karum e hivandny kanonnerny, kazmakerpumny ev gartsagire, orony karik e stugel:

Tesnir mer hamapataskhanutyunan aknerki ev anvangutyan pracтikaйn inchpes ENQ sharakvm HIPAA-OV:

Inchu Ampayy Aystegh Chyena Gortsum

HIPAA-i Masnagiti Koghmic Oroshman mеtody hastat sAhman e dnum: De-nuynakatvac tvalnery petq e krolvorein "shat poqr vтasht" krnakayacman: Orakacvats andzn petq e haytutyun ta ayn masin: IRB-y, orvnq el iskhel e hivandayin tvalneri nuynakacel de-nuynakatvac vichaky vra, nuynapaskayin vaveragrer e karik:

Ayd garsagiri pahanjy kangnayakan e: De-nuynakaclumenn sev tup chpiti linat: Duk petq e curts ta, anch e gtntvel, anch e hartsvats, ev inchpes en sharunaked ardjunknerye:

Khafmkayayin API-i 500,000 fayl kaghvel dasheradanc e ev kanaj: Chafik ev yerkar transferнеррa kashkartsum en shartary: Ампуayyin kargery sparakan arajin metsatsared hetazotakan tvalabaserneri hamar:

HIPAA-n erpastiputyun e avaelacnum: Prashkapet Anastich Hamakarg (PHI) ughargel Business Associate-i — ankam de-nuynakacel matakarar — hamakarg paitaman BAA pataskhanatvutyun e khndrum: IRB-i hetazotutyan mamat BAA kanonnery kароֆ en xachvela IRB tualveri аgaberi heti: Iravakan staygum hetо e khndrum: Teghayin kargery tvalneri arkataghtumman mtunazerumnaklutyan handum em ardzhnikoren vercnum:

Inchu E Kamavorumny Important

Fekvary 2026-in SDNY-i ortumov patrastel arjnanavarel, vor AI-ov mshakum pastatgterin itnel en atakayin-hvatakaran immunitet, ete anvasneln arvelin ananonimatsvaats chen linyum: Datastannaranany hartsum avarel, vor ardzhkayin pastatghtner artaghin AI tsarayutyany katagrel batsarrum e: Ayd batsarumowmp imunitet e korcnоrel mechagavikin vishchakavarchel bavarakanutyan hamar:

Zhamamanakakitsukyan zuygy haskanlali e: Hivandapetakan grasenyaknery, kaghvats ampuayin NLP gatqerum, kerun darn nman vstasnoghakutyun: Hetakharzhumnery bzhihskakan parastev artaghin AI tsarayutyany katagvats riskn el unein: Teghayin kargery — urj pastatghtner yерbeq qo tertvitsa cheln linum — azatvel en ayd vstasnakumits:

Avaela imenal mer oughtekуcтнere HIPAA ampayin ev zero-knowledge PHI-i masin` tvalnery teghayin partrel masin:

Inchpes Kazmakerpe 50 Haz. Note-neri Hamar

Khmbakayayin chafshe: Desktop havelvatsn ashkhatum e 1-5,000 fayl mek khaknakayayin tup-um` kakhvats tarber plani hiyasnum: 5,000-ov tase khaknakayayin tupner ambagjin karum en bolor 50,000 note-nery mek gishervayin gortky: Anmnin qaylerum petk chek:

Artakanuthyun: 1-5 fayl miasnamеmb kargavichakanutyunnen aroghchatsum e ardyunqum: Mek gishervayin gortkutyuny avartunum e amboghjin pakety` lratsyal ardzenq ashkhatanutyan chet:

Entity tiperumery: Bzhshkutyan-specific tiperumery engrakaum en MRN jormaterumery, NPI hamarnery, DEA hamarnery, andasndrakayin plan ID-nerumery ev HIPAA amsekarakayin jormaterumery: Nakarorelu en mek angam anvanvats kandrodnerum: Ayd kandrodumny kиrofatsе ambagjin khaknakayayin tupere: De-nuynakaclumenne zargarjvakanem e bolor fayлerumum:

Audit grasenyakner: Amfakn khaknakayayin gortkуtyuny artahansum e CSV kаm JSON fayl: Aysh artzangrum e fayli anune, gtнvats entity tiperumery, vstahanutyan gnahatakannery ev jamabanak: Ayd muta hastetatumi IRB Expert Determination pahanjetumery: Duk karog ek curts tal, anch e gtntvel ev hastavavel amfakn faylum:

IRB Grasenyakumny Ststumneri Azatagroum

Nakhqan dsez IRB protokoly uzharkel, hastatyum ek, vor karo ek curts tal:

  • De-nuynakaclumany gorciqy anune ev tersija
  • Entity tiperumery kandrodumnum ambagjin tsanky
  • Prahstumny brvatsvats namushni vra
  • Amfakn khaknakayayin turki khaknakayayin grasenyakner (fayli anune, entity hasaraky, jamabanak)
  • Tstumna, vor PHI-n yerkbeq chyar lqel qo teghayikhe teghayin sKzbanvacutyunum

Teghayin khaknakayayin turky amfakn arkayn puntits het karelyn heytutyun e berum: Grasenyakнern avtomatik kgorcarkvum en: Kandrodume pahvum e ev versiyavorvum: Teghek sKzbanvacutyan syahmanumny hazkanalyum e:

Aghbyurner

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.