Maoni ya Faragha ya Data
Makala za kitaalamu kuhusu usalama wa AI, ufuatiliaji wa GDPR, ulinzi wa data za afya, na mbinu bora za uanonymishaji wa PII.
Makala Zote
Garante Italia: Uzingatiaji wa AI na PII
Garante ya Italia ilimuadhibu OpenAI euro milioni 15 mwezi Desemba 2024 na ikapiga marufuku ChatGPT kwa muda mwaka 2023. Asilimia 63 ya makampuni ya Italia haina sera za usimamizi wa data za AI.
LGPD Brazil: CPF, CNPJ, na Ulinzi wa Data
LGPD inashughulikia Wabrazili milioni 215 na ANPD ilianza utekelezaji mkubwa mwaka 2024. CPF imegunduliwa kwa usahihi wa asilimia 45 tu na zana zilizofunzwa kwa Kiingereza.
AP ya Uholanzi: Faini ya €290M na Utekelezaji wa GDPR
AP ya Uholanzi ilitoa faini kubwa zaidi ya EU kwa uhamishaji wa data — €290M dhidi ya Uber. BSN (nambari ya kitaifa ya Uholanzi) inahitaji uthibitishaji wa Elfproef unaokosekana kwa 56% ya zana.
UODO Poland: PESEL, NIP na RODO
UODO iligundua 89% ya zana zilizotumwa hazitambui PESEL ya Kipolandi ipasavyo. Poland inashughulikia rekodi za wateja wa EU 2.3M kila siku. Uthibitishaji wa jedwali la PESEL, NIP.
ANSPDCP Romania: Utambuzi wa CNP na Ukaguzi
ANSPDCP iligundua 78% ya zana zinakosa CNP ya Kiromania na uthibitishaji sahihi. CNP inasimba jinsia, tarehe ya kuzaliwa, na kaunti ya kuzaliwa — athari za aina maalum ya GDPR.
IMY Sweden: Personnummer na Ukaguzi wa Luhn
IMY iligundua 45% ya zana za kawaida hazikosi personnummer ya Kiswidi. Samordningsnummer (offset ya 60) inakosekana kwa utekelezaji mwingi. Mazoezi ya haki za GDPR ya 79% ya Sweden.
CPR ya Denmark: Uthibitishaji wa Modulo-11 kwa GDPR
67% ya zana za NLP zinakosa uthibitishaji wa modulo-11 wa nambari ya CPR ya Kideni. Hatua 14 za utekelezaji wa afya za Datatilsynet mwaka 2024. Matumizi ya sekondari ya data ya afya.
Rodne Cislo ya Kicheki: Usimbuaji wa Jinsia na GDPR
Rodné číslo ya Kicheki inasimba jinsia kupitia usimbuaji wa mwezi wa offset ya 50 — ikifanya iwe data ya aina maalum ya Kifungu cha 9 cha GDPR. 67% ya makampuni ya Kicheki yanatumia zana za Kijerumani.
NAIH Hungary: TAJ-Szam na Adoazonosito Jel
Usahihi wa NER wa Kihungari ni 67% dhidi ya wastani wa EU 82% — tathmini ya NAIH 2024. Jedwali la ukaguzi lenye uzito la TAJ-szam na mapengo ya utambuzi wa adoazonosito jel.
HDPA Ugiriki: Utambuzi wa AFM na AMKA
AFM ya Kigiriki inagundulika kwa usahihi wa 52% tu na zana za kawaida. HDPA ilitoa maamuzi 89 mwaka 2024 — ongezeko la 162% kutoka 2022. Sekta za utalii na usafirishaji wa baharini zinakabili hatari tofauti.
My Number ya Japani: Verhoeff na APPI
63% ya zana za kawaida zinashindwa kutambua My Number katika hati za Kijapani. My Number hutumia algorithm ya Verhoeff — checksum ngumu zaidi ya kitambulisho cha kitaifa barani Asia.
Faili za Epstein: Kuangazwa kwa Rangi Nyeusi si Kufuta
Kutolewa kwa faili za Epstein za DOJ mnamo Desemba 2025 kulifunua kasoro kubwa ya kufuta: maandishi ya PDF yaliyoangaziwa kwa rangi nyeusi yanaweza kusomwa kwa njia ya kunakili-kubandika.
Haki ya Usiri wa Wakili-Mteja & AI mwaka 2026
Mahakama ya shirikisho ya Marekani ilitoa uamuzi mnamo Februari 2026 kwamba mazungumzo na AI hayahifadhiwa na haki ya usiri wa wakili-mteja.
Usimbuaji wa Ujuzi Sifuri dhidi ya Kuamini Sifuri
LastPass ilisimba data ya watumiaji wao pia — na $438M iliibwa hata hivyo. Hivi ndivyo tofauti kati ya usimbuaji wa upande wa seva na ujuzi sifuri wa kweli.
PII Iliyotengwa Kabisa: Kompyuta ya Nje ya Mtandao kwa Ulinzi
41% ya sera za usalama za biashara kubwa zinakataza ushughulikiaji wa wingu kwa hati zilizo siri.
Ugunduzi wa PII wa Lugha Nyingi kwa GDPR
Steuer-ID ya Ujerumani, NIR ya Ufaransa, na Personnummer ya Uswidi vyote vinahitaji mantiki tofauti za ugunduzi.
Uchaguzi wa Ufutaji Unaoweza Kurejeshwa dhidi ya wa Kudumu
GDPR inabainisha tofauti kati ya kutengua na kutia kivuli. Mahakama zinahitaji asili. Utafiti unahitaji utambulifu upya. Jifunza ni lini kutumia mbinu kila moja.
NER ya Lugha Nyingi: Kiingereza Kinashindwa Kiarabu
Mifano ya NER iliyofunzwa kwa Kiingereza inafikia usahihi wa 85-92%. Kiarabu na Kichina? Mara nyingi 50-70%. Jifunza kuhusu changamoto za kiufundi na jinsi ya kujenga mfumo wa kweli.
94% ya Biashara Ndogo Zilishambuliwa: Faragha ya Bei Nafuu
Biashara ndogo zinakabiliwa na vitisho sawa na makampuni makubwa lakini haziwezi kumudu zana za $800+/mwezi. Hivi ndivyo unavyopata ulinzi wa kiwango cha biashara kubwa kwa €3/mwezi.
Ugunduzi wa PHI: Snow Labs 96% dhidi ya GPT-4o
Zana zote za kuondoa utambulifu si sawa. Vipimo vya ECIR 2025 vinaonyesha alama za F1 kutoka 79% hadi 96%. Jifunze kwa nini usahihi ni muhimu na jinsi ya kutathmini zana.
Mahakama Zinaadhibu Mawakili kwa Makosa ya Ufutaji
Kuonyesha rangi maandishi katika Word si ufutaji. Mahakama zinaadhibu mawakili kwa kushindwa kiufundi ambako kunafichua taarifa za haki.
Tumia Claude na ChatGPT Bila Kuvujisha PII
Mwongozo wa msanidi programu wa kutumia wasaidizi wa AI kwa usalama. Sanidi muunganisho wa MCP Server kwa ulinzi wa PII usio na msururu katika Claude Desktop, Cursor, na VS Code.
Watumiaji 900K Waliibiwa Mazungumzo Yao ya AI
Nyongeza mbaya mbaya za Chrome ziliiba mazungumzo ya ChatGPT kutoka watumiaji 900,000+. Moja ilikuwa na beji ya 'Featured' ya Google.
$7.42M: Gharama za Uvunjaji wa Afya Zinaongoza
Sekta ya afya imekuwa tasnia yenye gharama kubwa zaidi ya uvunjaji wa data kwa miaka 14 mfululizo. Jifunze kwa nini PHI ina thamani kubwa na jinsi ya kuiilinda.
Anza Kulinda Data Yako Leo
Aina 267+, lugha 48, usalama wa kiwango cha biashara kwa bei za kuanzisha.
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, held by our hosting provider.
- 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 company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.
Hetzner holds 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
Automated checks run on every release.
Each surface gets its own sweep script and report.
Human reviewers spot-check the output each week.
We test detection against sample documents before each release.
A failing unit or integration run stops the release.
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.
Paid plans can buy top-up credits.
Credits reset at the end of each cycle.
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
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
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.