--- title: "数据隐私 - 安全与治理 - AI·外脑 | 思行致远" canonical: "https://siyuanailab.com/knowledge/ai-core/safety/data-privacy/" dateModified: "2026-06-29" datePublished: "2026-06-29" --- 打开 AI·外脑目录 AI·外脑 [返回专栏首页](https://siyuanailab.com/knowledge/) × 站内搜索 ⌕ 主要栏目 - [AI 核心概念](https://siyuanailab.com/knowledge/ai-core/) - [工作流](https://siyuanailab.com/knowledge/workflows/) - [工具配置与选型](https://siyuanailab.com/knowledge/toolkits/) - [信源与标准](https://siyuanailab.com/knowledge/sources/) - [场景案例 待开发](https://siyuanailab.com/knowledge/scenarios/) - [职业与能力](https://siyuanailab.com/knowledge/career/) 专题 - [从这里开始](https://siyuanailab.com/knowledge/start/) - [获取与安装](https://siyuanailab.com/knowledge/install/) - [建设日志](https://siyuanailab.com/knowledge/ops/) [AI 核心概念子目录](https://siyuanailab.com/knowledge/ai-core/) [基础概念 **9**](https://siyuanailab.com/knowledge/ai-core/foundations/) - [01-01 人工智能](https://siyuanailab.com/knowledge/ai-core/foundations/artificial-intelligence/) - [01-02 生成式 AI](https://siyuanailab.com/knowledge/ai-core/foundations/generative-ai/) - 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关键澄清： | 它不是什么 | 它是什么 | | --- | --- | | 不是只靠一句保密承诺 | 数据分类、访问控制、脱敏、留存和合规的组合 | ## 二、为什么重要 - AI 应用经常处理高价值上下文，隐私边界决定能否放心使用。 - 不懂它会怎样：把敏感数据直接发给不合适的模型或工具，可能造成不可逆泄露。 ## 三、日常怎么理解 - 用一句话讲给非技术朋友听：数据隐私就是别让 AI 乱看、乱记、乱传敏感信息。 - 常见场景：客户资料、聊天记录、财务数据、合同、员工信息。 ## 四、常见误解 | 误解 | 真相 | | --- | --- | | 只要模型强，数据给得越多越好 | 数据要按最小必要原则提供，并明确存储和使用规则。 | ## 五、延伸阅读 - 本知识库相关：记忆系统、API密钥即护栏 - [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) - [OpenAI Safety best practices](https://platform.openai.com/docs/guides/safety-best-practices) ## 适用场景与行动 **场景：**适合用来建立 AI 核心概念的共同语言。 **适合谁：**非技术创业者、AI 产品使用者、内容与业务负责人 **下一步：**先用一句话复述这个概念，再判断它和你的业务场景有什么关系。 这张卡片由杨思远基于真实工作流和实测经验整理。 如发现信息过时或有误，可通过 [/contact](https://siyuanailab.com/contact) 反馈。 [上一张：模型偏见](https://siyuanailab.com/knowledge/ai-core/safety/model-bias/) [返回当前分类](https://siyuanailab.com/knowledge/ai-core/safety/) [返回 AI·外脑入口](https://siyuanailab.com/knowledge/)