By George Curta · Last updated 2026-04-07
为什么 AI 数据泄露防护至关重要
像 ChatGPT、Claude 和 Copilot 这样的 AI 工具改变了我们的工作方式。但它们也创造了一个巨大的新攻击面。根据 LayerX 2025 年的研究，AI 现在是第一数据外泄渠道，超过了传统渠道，如电子邮件和 USB 驱动器。
现实世界中的 AI 数据泄露 (2025)
- Chrome 扩展泄露： 90 万用户通过恶意浏览器扩展被盗取 AI 对话 (2025 年 12 月)
- Urban VPN 泄露： 800 万用户的 AI 数据通过 VPN 服务被收集 (2025)
- 平均泄露成本： $488 万 (IBM 2024)，与 AI 相关的泄露趋势更高
AI 泄露防护能力
285 种以上的实体类型
检测姓名、社会安全号码、信用卡、API 密钥、医疗记录和 250 种以上可能通过 AI 泄露的 PII 类别。
48 种语言
为跨国团队提供全球覆盖。以英语、德语、法语、西班牙语、中文、日语和其他 42 种语言检测 PII。
可逆加密
AES-256-GCM 加密让您在需要时恢复原始值。在防止泄露的同时保持数据的实用性。
零知识
加密密钥永远不会离开您的控制。即使被迫，我们也无法访问您的数据。真正的零知识架构。
欧盟数据驻留
在 Hetzner 上 100% 德国基础设施。没有 AWS、Azure 或 GCP。无美国云法案暴露。完全符合 GDPR。
企业级准备
通过管理政策在整个组织中部署。团队管理、使用分析和集中密钥管理。
30 天实施路线图
第 1-7 天：评估
审核当前 AI 工具使用情况。识别高风险团队和数据流。将 Chrome 扩展部署给试点组。
第 8-14 天：政策
起草 AI 可接受使用政策。为您的敏感数据类型配置检测规则。设置团队管理。
第 15-21 天：推广
部署到所有团队。进行员工培训。为开发团队配置 MCP 服务器。
第 22-30 天：优化
审查检测准确性。根据反馈调整政策。设置持续监控和报告。
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
- Common questions
- Glossary
- How tokens work
- Security posture
- Where we comply
- What we detect
- Case studies
- Release notes
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
- 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.