By George Curta · Last updated 2026-04-07
人工智能与生成性人工智能保护
阻止数据泄露到ChatGPT、Claude和Copilot
77%的员工将敏感数据粘贴到人工智能工具中。生成性人工智能现在是数据外泄的首要渠道。
Chrome扩展在PII到达人工智能模型之前拦截。本地处理。零数据暴露。
企业
大规模GDPR合规与数据保护
大型组织处理数百万份包含个人数据的文档,跨多个部门。
集中式PII检测和匿名化,带审计跟踪、基于角色的访问和批量处理。
中小企业与初创公司
以初创公司定价提供企业安全
94%的中小企业在2024年遭到攻击。企业工具的费用为每月800美元以上。
相同的285+种实体类型和48种语言。免费开始,扩展到每月3欧元。
开发者
测试环境与CI/CD管道
开发团队需要真实的测试数据,而不暴露生产PII。
API集成,实现CI/CD管道中的自动化匿名化,生成安全的测试数据集。
法律
合同匿名化与电子发现
律师事务所必须从合同、法庭文件和发现文档中删除敏感信息。
精确的删除,带审计跟踪以确保法律合规和文档生成。
医疗保健
患者数据保护与HIPAA支持
医疗提供者必须保护患者信息,同时支持研究和分析。
符合HIPAA的医疗记录、研究数据和行政文件的匿名化。
金融
PCI-DSS合规与防欺诈
金融机构处理敏感客户数据,受严格的监管要求约束。
符合规定的匿名化,用于交易记录、客户数据和监管报告。
研究
学术数据共享与出版
研究人员需要共享数据集,同时保护参与者隐私。
一致的伪名化研究数据集,促进合作与出版。
政府
公共记录与FOIA合规
政府机构必须发布公共记录,同时保护公民隐私。
自动化删除FOIA请求、公共记录和跨机构共享。
数据主权
托管于德国,仅在欧盟内处理数据
跨境数据传输使组织面临 US Cloud Act 和 GDPR 罚款的风险
100% 德国基础设施,不使用美国云服务提供商。设计上符合 Schrems II
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.