技能 data-security-expert

📦

data-security-expert

Name: data-security-expert
Author: 图灵数盟

內容修訂 r1 安全 📁 檔案系統存取⚙️ 外部命令🌐 網路存取

Assess and Strengthen Data Security

Organizations need consistent controls across the data lifecycle. This skill structures classification, risk assessment, MLPS planning, safeguards, audits, and response deliverables.

支援: Claude Codex Code(CC)

🥉 77 銅級

Maintain this Skill? Claim it 🥉 77 銅級

使用我的智慧代理安裝

將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。

智能體請求

Review the Skillstore skill "data-security-expert" from https://skillstore.io/skills/dhturing-data-security-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-security-expert/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/dhturing-data-security-expert.md 已簽名 manifest GET /api/skills/dhturing-data-security-expert/manifest 已簽名 lockfile GET /api/skills/dhturing-data-security-expert/lockfile

測試它

正在使用「data-security-expert」。 Classify customer profiles, public product data, and high-value transaction records.

預期結果:

Customer profiles: general sensitive data, internal level 3, with encryption and controlled access.
Public product data: general public data, internal level 1, with integrity controls.
High-value transaction records: assess industry thresholds before deciding important status; apply level 4 controls meanwhile.

正在使用「data-security-expert」。 Outline a security assurance package for a financing review.

預期結果:

Management evidence: approved policies, ownership records, training logs, and risk acceptance decisions.
Technical evidence: access reviews, encryption design, masking rules, immutable audit logs, backup tests, and vulnerability remediation.
Assurance evidence: MLPS filing, assessment reports, incident exercises, audit results, and tracked corrective actions.

安全審計

安全

v1 • 2026/9/27 開啟版本化報告

All 12 static findings are false positives caused by documentation syntax, cited installation paths, public links, and entropy heuristics on multilingual Markdown. The reviewed files contain no automated command execution, network requests, obfuscated payloads, or prompt injection language.

已掃描檔案

437

分析行數

複核項目

已忽略的誤報

風險因素

📁 檔案系統存取 (2)

profile.md:38 profile.md:38

⚙️ 外部命令 (3)

SKILL.md:20 SKILL.md:26 SKILL.md:33

🌐 網路存取 (2)

SKILL.md:6 SKILL.md:12

最近一次完成的靜態與語意稽核未偵測到已確認的安全發現。這並不能證明該 Skill 沒有副作用。

審計單位: codex

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

https://skillstore.io/skills/dhturing-data-security-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown 徽章

[![Skillstore security assessment](https://skillstore.io/badges/skills/dhturing-data-security-expert/security.svg)](https://skillstore.io/skills/dhturing-data-security-expert?utm_source=security_passport_badge)

HTML 徽章

<a href="https://skillstore.io/skills/dhturing-data-security-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-security-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

嵌入卡片

<iframe src="https://skillstore.io/embed/skills/dhturing-data-security-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

學術引用 (APA · BibTeX · CFF)

APA 引用

图灵数盟. (2026). data-security-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-security-expert/audits/1

BibTeX 引用

@techreport{dhturing-data-security-expert-2026,
  author = {图灵数盟},
  title = {data-security-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-data-security-expert/audits/1},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "data-security-expert security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "图灵数盟"
date-released: "2026-09-27"
url: "https://skillstore.io/skills/dhturing-data-security-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-data-security-expert:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore 評分

評分依據證據置信度: 中

架構

可維護性

內容

社群

規範符合度

你可以打造什麼

Classify Enterprise Data

Create a classification table and important data catalog from an inventory, ownership details, scale, sensitivity, and business impact.

Plan Security Remediation

Map lifecycle gaps to access, encryption, masking, logging, backup, disposal, and incident response controls.

Prepare Assurance Evidence

Draft evidence checklists and report structures for MLPS assessment, financing review, registration, audit, or DSMM improvement.

試試這些提示

Classify a Data Inventory

Classify these assets as core, important, or general under GB/T 43697-2024. State assumptions, evidence gaps, and controls: [asset list].

Review Lifecycle Controls

Assess controls across collection, storage, use, sharing, transfer, and disposal. Prioritize gaps by threat, vulnerability, impact, owner, and deadline.

Build an MLPS Plan

Evaluate this system for MLPS scope and likely level. Provide rationale, filing steps, remediation work, evidence needs, timing, and external dependencies.

Design an Assurance Program

Create an integrated security program for [organization]. Include classification, risk register, lifecycle controls, DSMM roadmap, audit schedule, incident response, and evidence.

最佳實務

Provide current inventories, architecture diagrams, control evidence, and applicable industry requirements.
Separate confirmed facts, assumptions, evidence gaps, and recommendations in every deliverable.
Validate classifications and compliance conclusions with accountable owners and qualified external assessors.

避免

Do not assign important data status solely from a generic example.
Do not treat encryption as sufficient without access control, key management, logging, and recovery.
Do not present draft guidance as regulator approval or a completed independent assessment.

常見問題

Can this skill determine whether data is important data?

It can structure the assessment and compare supplied facts with applicable catalogs. Final classification requires industry context and accountable review.

Does it perform an MLPS assessment?

It prepares scope, likely level, gaps, evidence, and remediation plans. A qualified assessment body must perform formal testing.

Can it create an important data catalog?

Yes. Provide the inventory, business purpose, scale, precision, affected parties, geography, and industry requirements.

Which lifecycle stages does it cover?

It covers collection, storage, use, sharing, transfer, and disposal, including controls and audit evidence.

Can it recommend encryption and masking controls?

Yes. It can propose control requirements, but implementation choices require architecture, threat, performance, and legal details.

Is the guidance limited to China?

The knowledge base centers on Chinese laws and standards. Supply other jurisdictional requirements before applying it elsewhere.