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Mfiduo wa PII 3.8 kwa Siku katika Timu za Usaidizi

Kila wakala wa usaidizi anayetumia ChatGPT hufanya wastani wa ubandikaji wa data nyeti 3.8 kwa siku. Kwa timu ya watu 100, hiyo ni matukio 380 ya mfiduo wa GDPR kila siku.

April 18, 20268 dakika kusoma
accidental PII exposuresupport team ChatGPTCyberhaven 3.8 pastesworkflow PII protectionGDPR daily exposure

Hisabati ya Mfiduo wa Kila Siku wa PII

Utafiti wa Cyberhaven uligundua kwamba wafanyakazi wa biashara hufanya wastani wa ubandikaji wa data nyeti 3.8 katika ChatGPT kwa mtumiaji kwa siku. Kwa timu ya usaidizi ya watu 100, hiyo ni matukio 380 ya rekodi za wateja zinazoingia ChatGPT kila siku.

Kila tukio linaweza kuwa uvunjaji wa upunguzaji wa data wa GDPR chini ya Ibara ya 5(1)(c). Ibara hiyo inahitaji taarifa za kibinafsi kuwa "zinazofaa, zinazohusiana na zizuiwe kwa kile kinachohitajika."

Hizi si wafanyakazi wasio na nidhamu wanaopuuza sera. Kielelezo cha 3.8 kinaakisi kazi ya kawaida. Mawakala hunakili barua pepe za wateja kuandaa majibu. Wanabandika maandishi ya malalamiko kupata mapendekezo ya huruma. Wanajumuisha maelezo ya akaunti kupata majibu yanayojua muktadha. Kila ubandikaji ni hatua halali ya tija inayobeba PII pamoja nayo tu.

Mafunzo ya Tabia Hayatatui Hili

Ukaguzi wa EU wa 2024 uligundua kwamba asilimia 63 ya data ya watumiaji wa ChatGPT ilikuwa na taarifa zinazoweza kutambuliwa za kibinafsi. Ni asilimia 22 ya watumiaji peke yake waliojua wangeweza kujiondoa kupitia mipangilio ya zana. Maudhui mengi yanayobandikwa katika msaidizi wa AI yana PII. Watumiaji wengi hawajui kuhusu vidhibiti. Matokeo yake ni mfiduo wa kila siku kwa kiwango kikubwa.

Mafunzo ya sera yanakabiliwa na tatizo la msingi. Tabia ya kunakili na kubandika ina umri wa miongo kadhaa. Watumiaji wamekuwa wakinakili na kubandika maandishi tangu siku yao ya kwanza kwenye kompyuta. Kuunganisha zana ya mazungumzo ya AI kama lengo la kubandika kunaongeza marudio mapya. Haibadilishi tabia.

Sera ya "usibandike PII ya mteja katika msaidizi wa AI" inauliza mawakala kuingiza hatua ya uainishaji -- "je, maandishi haya yana PII?" -- katika kitendo cha tabia ambacho hakina kusimama kwa asili. Athari za mafunzo zinapungua. Matokeo ya jumla ya maamuzi ya ubandikaji 380 kwa siku ni hatari ya uzingatiaji ambayo sera peke yake haiwezi kushikilia.

Mahali Ambapo Vidhibiti vya Kiufundi Vifanye Kazi

Suluhisho hufanya kazi katika hatua ya kubandika yenyewe. Nyongeza ya kivinjari inakamata maudhui ya ubao wa kunakili wakati wakala anapobonyeza kubandika, kabla maandishi hayajafikia uga wa ingizo. Wakala anaona modali ya muhtasari. Inaonyesha kilichogunduliwa na kilichofutwa utambulisho wake kabla maandishi hayajatumwa.

Hii si kidhibiti cha kuzuia. Mawakala wanaweza kuendelea, kuchagua tofauti, au kusimama. Ni hatua ya uwazi. Inaongeza dakika moja ya mwonekano kwa kitendo ambacho kwa kawaida ni cha kiotomatiki.

Fikiria kiongozi wa timu ya usaidizi wa biashara ya mtandaoni ya Ujerumani anayeandaa majibu kwa malalamiko ya wateja. Mtiririko wa kazi unabaki sawa: nakili malalamiko, bandika kwenye ChatGPT, tengeneza jibu. Nyongeza inaongeza ukaguzi wa sekunde mbili. Wakala anaona kwamba majina, anwani, na nambari za amri ziligunduliwa. Wakala anabonyeza "endelea". Zana inapokea toleo lililofutwa utambulisho. Uvunjaji wa uzingatiaji haustokei.

Mwongozo wetu wa uzingatiaji wa GDPR unashughulikia msingi wa kisheria kwa vidhibiti hivi. Angalia pia ulinganisho wetu wa sera ya AI dhidi ya vidhibiti vya kiufundi na mwongozo wa DLP ya kivinjari kwa ChatGPT kwa maelezo ya utekelezaji.

Vyanzo

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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.