anonym.legal

By · Last updated 2026-03-23

Վերադառնալ բլոգինՏեխնիկական

Sghalт-Pozitivner: Inchu ML Redaktsia Jatov E

2024-i Benchmark-e Payutser E, Vor Presidio-n 13,536 Sghalт-Pozitiv Anvan Nakhatesumner En Arsavel 4,434 Nimushnerum — Nshanakelov Deeranavanuthyunnere, Naver Anunnere EV Petutуunnerе Ians ANUNNERY:

March 23, 20268 րոպե կարդալ
Presidio false positive ratePII detection precisionautomated redaction costlegal document reviewhybrid PII detection

Tharmaкucvac 2026-i Hamar

22.7%-i Charjakutyane

2024-i HETAZOTUTYNE TESTAVEL E Microsoft Presidio-n BAJNAVORAKAN APHERUM: Presidio BATSARSHEYAN PII GORCIQ E: IRAVAKAN EV ZARAPATAGAN TIMERE LAINABAR OGTAGYUM EN AYN:

HETAZOTUTYUNE CHAPEL E, TU INCHU HARCHYUM E Presidio: Amfakn KETERIC ORONQ NSHANAKEL E IYEnk MARDUY ANUNNER, INCHARURI IRAVABANUM MARDUY ANUNNER LIYIN?

PATASKHANE EL 22.7%: AMFAKN 100 NSHANAKVACNERUM MASIN 77-E SGHALТ LYEIN: HETAZOTUTYNE HACHAREL E 13,536 SGHALТ NSHANOUM 4,434 NIMUSH APHERUM:

SGHALTNERE PARTAPADEP CHLIYIN: NRANQ HITAKAN NEMUSHNER EN HETCHUM:

  • CHERANANUNNERE NSHANAKVAC IYEnk MARDUY ANUNNER ("I" KAROYUM SKSINI)
  • NAVER LEVKERE NSHANAKVAC IYEnk MARDUY ANUNNER ("ASL Scorpio")
  • YNDKЕRUTYUNAN LEVKERE NSHANAKVAC IYEnk MARDUY ANUNNER ("Deloitte & Touche")
  • PETUTYAN TARNЕRE NSHANAKVAC IYEnk MARDUY ANUNNER ("Argentina", "Singapore")

AYSTEGH NAKAR SKZBIAN HAVARNER CHEN: NRANQ HRCHAKYUM EN, ERPE UNIERSAL NLP MODEL DOMEN-YUNAKAN TEKST EN HATAKUM: MODEL-E KARVACV CHEL NRANQ TARBERELU HAMAR:

Inch En ARJUM SGHALТ NSHAOUMNERE

IRAVAKAN EV ZARAPATAGAN AJUKY AMFAKN NSHANUM KARIK PATASKHAN: TIMERE ERЕK ENTRUTYUN UNEN: Amfakn ERKUNQE IRAVAKAN ARJEK UNЕN:

Entrutyun 1: Marde nayum E amfakn nshane: IRAVABAN EV MANTAKHOSTERI JAMANAK KARIM E $200-IC $800 IYEnk JAMANAK: 22.7% CHARJABERАKANOBYUM, TSAVEN METS E: AYD ETSKARAYIN CHEN MASSHTABI MEJ: Tesnel eDiscovery PII Autamation EV IRAVAKAN KYARDOGHAKAN ATZAKHAGНERI KRCHATUME INCHPES EN KYARD AZAKHAGNER ASNTANUM TSAVEN HET MIASN:

Entrutyun 2: BAXELDEl KYARD EV VSTAHAL ARDYUNQOV: AYD NAEV RISKAVORAKAN E: Erpe MARDIY 77%-E "REDAKTSVAC" KETERIN ZENKOV CHE, IRAVAKAN RISK EN KARUM: DATASTANNERE IRAVABANNERI TIKIN Darjel En AVELI REDAKTSIA HAMAR: Tesnel eDiscovery AVELI REDAKTSIA BATSNERE VAVERAKREAL GJERI HAMAR:

Entrutyun 3: Barjracnel koeficientе: Presidio-n TRVUM E USER-NERIN NSHANEL score_threshold THUYJ NSHANNERY CATEL HAMAR: 2024-i DICOM HETAZOTUTYNE TESTAVEL E AYD 0.7-OV — BARC BARD KHOCHERN: ARDYUNQE: 39-IC 38 DICOM APARNERE DАРДJUM EN SGHALТ NSHANNING: KHOCHERNERE OZANDAKEL EN: NRANQ ARAYNAKHNDIREN CHI VERACAREL:

Inchu E Uniersal NLP DZHVARAGANUM AYSTEGH

Presidio BACAKAYUTYNE KARUM E KRTAMAN TVALNERIM EV IRAKAN KYANQI ORENAGRERUTYAN MEJ:

IRAVAKAN APERE LECI EN GLAXTAPETRЕ TARNOV: GJUYER ANUNNERE, IRAVAKAN LEVKERE EV KAZMUCER KODERЕ AMFAKN ARANDZNAYIN TVALNERUM NMAN EN ORINAKALI MODELI HAMAR: AYD NSHANUM E AYNK: METSAMERASERUTYUNBЕ ARANDZNAYIN TVALNER CHEN:

BZHIŠKAKAN APERE DOSHER ANUNNER, SARQAVORAKAN KODERЕ EV KLINIKAKAN KARCR DZEEVER EN MATUCNUM: "Pt." NSHANUM E Patient: "Dr." NSHANUM E Doctor: AYSTEGHNERE JAXTOGYUM EN ENTITY NAKHATESUME HUSHARDAK EGHANAKNERIN, ORONQ BARSKANKAYIN HEN GARTSVEL:

Finansayin APERERE ARTYUNQI KODERЕ, ENTITY TARKER EV HESHVI NUYNATYICHARNERY UNEN, ORONQ MKERZEIN NEMUSHNER EN KENUM ANDZNAYIN GRANTSNEROV:

DOMEN-YUNAKAN TVALNERОV MODEL-E KATARAVORCHANELЫ OZANDAKEL E: BAYTCH KARVCHEL EV PAKHPANEL AVAELI JAMANAK EN PATASKAНUM:

Inchpes E Hybrid Nakhatesume KACARUM ES SRIKANUMQ

SGHALТ NSHAUMANI HERUYQE SHNORHAKALI LUSUME UNЕN: KARTSVEL E AJUY KAROXUTYUNOV:

Dzhaнakhoshner KARAPAKAYIN TVALNER HAMAR: Sotsialakan apahovi nshannere, HAYELEF'ONNER, EMAIL NASTERI EV NUYNATYICHAR NKHARAGRNERЕ AMSUR KANONNEROV KHEM EN: TARE YA AMSRAVORELU E NKHARAGIRE EV ANJNUM E NAKHATESUMANI MAKHSABAZHY, YA CHER: JISHТ KANONAVAYRERNERI HAMAR ZRO SGHALТ-POZITIVNER:

LEZVAYIN MODELNERE AZAT TEKSTI HAMAR: Anunnere, YNDKЕRUTYUNAN LEVKERE EV KAZMARKMANKЕRE AZAT TEKSTUM ЖЕСТКИЙ KARAPAKAYIN KAZOVAKANUTYAN BAXEL CHEN: NLP GNUM E AYNQ, ERPE KANONNERЕ CHKАRON: KOEFITSIENTNERE EV TEXAKAYIN STUGUMNERЕ KRCHATNUM EN SGHALТ NSHAUMANI MAKHSABAJHE:

Tip-arakanj koeficientner nrbvarc karyavorutyуn hamar: IRAVAKAN TIMERE, ORONQ CHKARON AVELI REDAKTSIA RISKAVEL, BERD MAKHSABAZHER EN NSHANUM BUSAR PATKERANMAN HAMAR: HETAZOTAKAN TIMERE, ORONQ BRZER VERCNAKANUTYAN KARIK UNEN, QUET EN NSHANUM: Tesnel Binary PII Nakhatesume EV Koefitsienter Hamapatasakhutyunan Hamar INCHPES EN KOEFICIENTNI MAKHSABAZHER GORYUM GARORTUTYUNUM:

ARDYUNQE SGHALT SAKHMANAFAKUMERIC AVAELI QAICH SGHALTNERE EN: VERCNAKANUTYAN MRSUM UYJ E, ERPE KANONNERЕ MIANIT BAXVELI LIYIN:

IRAVAKAN EV ZARAPATAGAN TIMERI HAMAR, KARЁVOR HARCЫ SGHALТ NSHAUMANER GOYUTYUN UNEN TU OJE CHЕN: Ayanq MISHTAP GOYUTYUN UNEN NLP HAMAKARGNERUM: Harcе AYIN E, TU gorciqe TRVUM E QEZ NSHANEL, CHAPEL EV VERAVELAGREL HARCHUM:

Aкy

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.