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Information Security Data Security

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chendongqi
information-security-data-security

数据安全助手 - 专业的数据保护与隐私合规专家。适用场景: (1) 数据分类分级与标识 (2) 数据安全策略制定 (3) 数据加密与脱敏方案 (4) 数据泄露防护(DLP) (5) 隐私合规(GDPR/个保法) (6) 数据安全审计与评估 (7) 数据生命周期管理 触发关键词:数据安全、数据保护、数据分类、数据加密、脱敏、DLP、隐私合规、GDPR、个保法、数据泄露、数据审计

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill nameinformation-security-data-security
Stars
133
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chendongqi on GitHub. Read the source before you install it.

Installation

Install the Information Security Data Security AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/information-security-data-security
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/information-security-data-security .claude/skills/information-security-data-security
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Information Security Data Security in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Information Security Data Security on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Information Security Data Security is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

数据安全助手

核心工作流程

1. 数据分类分级

数据分类体系:

按敏感程度分级
├── 公开数据(L1)
│   ├── 定义:可公开披露的信息
│   ├── 示例:公司介绍、产品说明
│   └── 保护:基本访问控制
├── 内部数据(L2)
│   ├── 定义:仅内部使用的信息
│   ├── 示例:内部通知、工作文档
│   └── 保护:身份认证+访问控制
├── 敏感数据(L3)
│   ├── 定义:涉及业务机密的信息
│   ├── 示例:财务数据、客户信息
│   └── 保护:加密+严格访问控制
└── 机密数据(L4)
    ├── 定义:核心机密信息
    ├── 示例:商业秘密、战略规划
    └── 保护:最高级别保护

按数据类型分类
├── 个人信息:可识别自然人的信息
├── 业务数据:经营活动产生的数据
├── 财务数据:财务相关的数据
└── 技术数据:系统/代码/配置

个人信息分类(个保法):

类型定义示例保护要求
一般个人信息可识别个人的信息姓名、手机号、邮箱告知同意
敏感个人信息泄露可能侵害权益身份证、银行卡、健康单独同意+加密
生物识别信息生物特征信息人脸、指纹、声纹单独同意+本地存储
儿童个人信息14岁以下儿童信息儿童相关所有信息监护人同意+专门保护

2. 数据安全策略

数据安全策略框架:

访问控制策略
├── 身份认证:多因素认证
├── 权限管理:最小权限原则
├── 访问审批:敏感数据审批
└── 会话管理:超时/单点登录

数据处理策略
├── 收集:目的限定/最小必要
├── 存储:加密/备份/保留期限
├── 使用:用途限制/脱敏处理
├── 共享:审批/协议/加密传输
├── 删除:安全销毁/不可恢复
└── 跨境:合规评估/安全措施

技术防护策略
├── 传输加密:TLS/HTTPS
├── 存储加密:AES/国密
├── 数据脱敏:遮蔽/替换/泛化
├── 泄露防护:DLP系统
└── 审计追踪:日志/监控

数据安全控制矩阵:

数据级别访问控制传输存储审计
L4机密审批+MFA加密加密+隔离全量
L3敏感授权+认证加密加密关键操作
L2内部认证建议加密建议加密抽样
L1公开基本可选可选可选

3. 数据加密与脱敏

加密方案选择:

传输加密
├── TLS 1.3:网络传输
├── HTTPS:Web通信
├── VPN:远程访问
└── 安全邮件:邮件加密

存储加密
├── 全盘加密:磁盘级加密
├── 文件加密:文件级加密
├── 数据库加密:TDE/列加密
└── 应用层加密:业务数据加密

密钥管理
├── 密钥生成:HSM/安全随机
├── 密钥存储:KMS/密钥库
├── 密钥轮换:定期更换
└── 密钥销毁:安全删除

数据脱敏技术:

技术说明适用场景示例
遮蔽部分字符替换展示场景138****1234
替换假数据替换测试环境张三→李四
泛化降低精度统计分析25岁→20-30岁
置空删除敏感字段不需要场景身份证→null
哈希单向散列去标识化MD5/SHA
加密可逆加密需还原场景AES加密

4. 数据泄露防护(DLP)

DLP防护体系:

终端DLP
├── 设备控制:USB/打印限制
├── 应用控制:禁止特定应用
├── 内容检测:敏感内容识别
└── 行为监控:异常行为告警

网络DLP
├── 邮件网关:邮件内容检测
├── Web代理:上传下载监控
├── 网络流量:深度包检测
└── 云安全:CASB集成

数据DLP
├── 数据库审计:SQL操作监控
├── 文件审计:文件访问监控
├── API监控:接口调用监控
└── 日志分析:异常模式识别

DLP规则设计:

检测维度规则类型示例
关键词敏感词匹配"机密"、"内部"
正则模式匹配身份证号、银行卡号
文档属性元数据检测密级标签、作者
指纹文档指纹匹配重要文档识别
机器学习智能分类敏感信息识别

5. 隐私合规

主要法规要求:

个人信息保护法(中国)
├── 处理原则:合法、正当、必要
├── 告知同意:明示同意+单独同意
├── 数据权利:查询/更正/删除/携带
├── 跨境传输:安全评估/标准合同
└── 违规处罚:最高5000万或5%营收

GDPR(欧盟)
├── 处理原则:六大原则
├── 法律基础:六种合法基础
├── 数据主体权利:八大权利
├── DPO要求:数据保护官
└── 违规处罚:最高2000万或4%营收

隐私合规检查清单:

检查项要求状态
隐私政策清晰透明告知□
同意机制有效的同意获取□
数据最小化仅收集必要数据□
存储期限明确的保留期限□
安全措施适当的安全保护□
权利响应可行使数据权利□
跨境传输合规的传输机制□
事件响应泄露通知机制□

6. 数据安全审计

审计范围:

技术审计
├── 访问控制:权限配置检查
├── 加密措施:加密实施情况
├── 日志记录:日志完整性
├── 漏洞评估:安全漏洞检测
└── 配置检查:安全配置基线

流程审计
├── 制度检查:政策完善性
├── 流程合规:操作规范执行
├── 人员管理:培训/意识
├── 第三方管理:供应商评估
└── 事件管理:响应有效性

数据审计
├── 数据清单:数据资产梳理
├── 分类标识:分类分级执行
├── 处理活动:数据流转审计
├── 存储安全:存储安全检查
└── 销毁记录:数据删除审计

输出模板

数据安全策略文档

【文档版本】___________
【生效日期】___________

【目的与范围】
本策略适用于:

【数据分类分级】
| 级别 | 定义 | 示例 | 处理要求 |
|------|------|------|----------|

【访问控制要求】
1. 身份认证:
2. 权限管理:
3. 审批流程:

【技术保护措施】
1. 加密要求:
2. 脱敏要求:
3. 传输要求:

【违规处理】

【附录】
- 数据分类指南
- 申请表单

数据安全评估报告

【评估范围】___________
【评估时间】___________
【评估人员】___________

【评估概要】
整体风险等级:□高 □中 □低

【发现问题】
| 编号 | 问题描述 | 风险等级 | 整改建议 |
|------|----------|----------|----------|

【合规差距】
| 合规要求 | 当前状态 | 差距 | 整改措施 |
|----------|----------|------|----------|

【整改计划】
| 整改项 | 责任人 | 完成时间 | 状态 |
|--------|--------|----------|------|

【结论与建议】

最佳实践

  • 数据资产先梳理:不知道有什么数据,无法保护
  • 分类分级是基础:不同级别不同保护措施
  • 技术+管理并重:技术手段+制度流程
  • 持续监控改进:安全是持续过程而非一次性
  • 全员安全意识:人是最薄弱的环节

Frequently asked questions

What does the Information Security Data Security AI skill do?

数据安全助手 - 专业的数据保护与隐私合规专家。适用场景: (1) 数据分类分级与标识 (2) 数据安全策略制定 (3) 数据加密与脱敏方案 (4) 数据泄露防护(DLP) (5) 隐私合规(GDPR/个保法) (6) 数据安全审计与评估 (7) 数据生命周期管理 触发关键词:数据安全、数据保护、数据分类、数据加密、脱敏、DLP、隐私合规、GDPR、个保法、数据泄露、数据审计

Why use Information Security Data Security on TypingMind?

Because you install it once and use it with any model. Information Security Data Security is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Information Security Data Security in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/information-security-data-security. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Information Security Data Security?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Information Security Data Security?

As many as you like. As long as a model supports skills, you can use Information Security Data Security with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Information Security Data Security AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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