スキル data-security-expert

📦

data-security-expert

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

コンテンツリビジョン r1 安全 📁 ファイルシステムへのアクセス⚙️ 外部コマンド🌐 ネットワークアクセス

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.

対応: Claude Codex Code(CC)

🥉 77 ブロンズ

Maintain this Skill? Claim it 🥉 77 ブロンズ

自分のエージェントでインストール

このリクエストをエージェントにコピーしてください。正規の Skill ページとマニフェストが含まれています。

エージェントリクエスト

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.

エージェントは引き続き計画を提示し、セキュリティポリシーで必要な確認を求める必要があります。

エージェントが読めるリソース

AI エージェント、クローラー、スクリプトがページ全体ではなく整理されたコンテキストを必要とする場合は、これらのリンクを使ってください。

Markdown詳細 GET /skills/dhturing-data-security-expert.md 署名済み manifest GET /api/skills/dhturing-data-security-expert/manifest 署名済み lockfile GET /api/skills/dhturing-data-security-expert/lockfile

テストする

「data-security-expert」を使用しています。 Classify customer profiles, public product data, and high-value transaction records.

期待される結果:

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.

「data-security-expert」を使用しています。 Outline a security assurance package for a financing review.

期待される結果:

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.

セキュリティ監査

安全

v1 • 2026/9/27 バージョン別レポートを開く

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.

スキャンされたファイル

437

解析済み行数

レビュー項目

誤検知を無視

リスク要因

📁 ファイルシステムへのアクセス (2)

profile.md:38 profile.md:38

⚙️ 外部コマンド (3)

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

🌐 ネットワークアクセス (2)

SKILL.md:6 SKILL.md:12

最新の完了済み静的・セマンティック監査では、確認済みのセキュリティ検出事項は見つかりませんでした。これは、スキルに副作用がないことを証明するものではありません。

監査者: codex

このレポートを共有・引用

バージョン付き評価レポート、中立的なバッジ、埋め込みカード、引用を共有できます。Skillstore は証拠を報告しますが、この Skill が安全かどうかは判断しません。

バージョン別レポートを開く

レポートリンクをコピー

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

Markdownバッジ

[![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バッジ

<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>

埋め込みカード

<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>

学術引用 (APA · BibTeX · CFF)

APA形式の引用

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形式の引用

@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 スコア

このスコアの理由証拠の信頼度: 中

アーキテクチャ

保守性

コンテンツ

コミュニティ

仕様準拠

作成できるもの

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.

これらのプロンプトを試す

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.

ベストプラクティス

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.

回避

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.

よくある質問

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.