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HIPAA 合规的 ChatGPT:浏览器端 PHI 防护

77% 的员工每周至少向 AI 工具分享一次敏感工作信息。实时浏览器 PHI 拦截可将泄露事件减少 94%。

April 20, 20268 分钟阅读
HIPAA ChatGPT complianceclinical AI learningPHI browser protectionmedical education AIreal-time PHI interception

临床 AI 的合规困境

医生和医学生每天都在使用 ChatGPT 和 Claude。他们查询药物剂量、检索诊断信息、审阅护理方案。这些工具确实有用。

然而,将真实患者数据粘贴到这些工具中存在 HIPAA 合规风险。文本会传输到 AI 服务商的服务器。若该服务未签署业务伙伴协议(BAA),这一行为本身即构成 HIPAA 违规。标准版 ChatGPT 和 Claude 账号均不包含临床使用所需的 BAA。

现有选择都不理想:带着真实数据使用 AI,冒着违规风险;或在粘贴前手动逐条脱敏——这一繁琐步骤往往被忙碌的临床医生省略。省略这一步,恰恰造成了整套流程原本要防止的数据泄露。

人工审核为何失效

HIPAA 安全港要求删除 18 类标识符。医生能识别出患者姓名和日期,但有些标识符容易遗漏。

地理子标识符是一个典型例子。年龄与入院日期的组合是另一个——根据 HIPAA 规定,两者合并可构成一对受保护的标识符。在时间压力下,这些规律并不直观。

Menlo Security 2025 年的研究发现,实时浏览器 PHI 拦截可将泄露减少 94%。这一数字揭示了临床医生与自动化工具在识别能力上的差距。Cyberhaven 的数据进一步印证了问题的规模:77% 的员工每周至少向 AI 工具分享一次敏感工作数据

浏览器扩展的作用

Chrome 扩展程序在提交时即对文本进行检查,在提示词到达 AI 之前完成拦截。医生会看到一个简短预览,显示发现了哪些 PHI、哪些内容将被脱敏处理。

这不是强制拦截。医生可以选择继续、编辑或终止操作。它只是在原本快捷的流程中增加了一次简短的确认。

以一位使用 Claude 进行案例教学的内科医生为例。他粘贴了一段已经审核过的病例记录,扩展程序随即进行二次检查。如果记录本身已足够干净,不会出现任何提示,操作继续进行。如果有细节遗漏——比如一对日期组合,或某个小城镇的名称——工具会率先发现。

这一模式非常适合临床工作场景:医生始终掌控主动权,工具则为人类容易忽视的规律提供一道安全防线。

如需了解工具性能基准,请参阅我们的 PHI 检测准确率对比。BAA 规则和安全措施请参考 HIPAA 云端零知识指南。部署详情请查阅浏览器 DLP 指南

参考来源

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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.

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Email support@anonym.legal.

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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.
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Plans in plain words

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One credit covers one short job.

Long jobs use a few credits each.

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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.