스킬 data-security-expert

📦

data-security-expert

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

콘텐츠 리비전 r1 안전 📁 파일 시스템 접근⚙️ 외부 명령어🌐 네트워크 접근

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.

지원: Claude Codex Code(CC)

🥉 77 브론즈

Maintain this Skill? Claim it 🥉 77 브론즈

내 에이전트로 설치

이 요청을 에이전트에 복사하세요. 공식 Skill 페이지와 매니페스트가 포함되어 있습니다.

에이전트 요청

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.

에이전트는 계속해서 계획을 표시하고 보안 정책에서 요구하는 확인을 요청해야 합니다.

에이전트가 읽기 쉬운 리소스

AI 에이전트, 크롤러, 스크립트가 전체 페이지 대신 정리된 컨텍스트만 필요할 때 이 링크를 사용하세요.

Markdown 상세 GET /skills/dhturing-data-security-expert.md 서명된 manifest GET /api/skills/dhturing-data-security-expert/manifest 서명된 lockfile GET /api/skills/dhturing-data-security-expert/lockfile

테스트해 보기

"data-security-expert" 사용 중입니다. Classify customer profiles, public product data, and high-value transaction records.

예상 결과:

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" 사용 중입니다. Outline a security assurance package for a financing review.

예상 결과:

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.

보안 감사

안전

v1 • 2026. 9. 27. 버전별 보고서 열기

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.

스캔된 파일

437

분석된 줄 수

검토 항목

오탐 무시됨

위험 요인

📁 파일 시스템 접근 (2)

profile.md:38 profile.md:38

⚙️ 외부 명령어 (3)

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

🌐 네트워크 접근 (2)

SKILL.md:6 SKILL.md:12

가장 최근에 완료된 정적·의미론적 감사에서 확인된 보안 발견 사항이 감지되지 않았습니다. 이는 스킬에 부작용이 없음을 증명하지 않습니다.

감사자: codex

이 보고서 공유 및 인용

버전이 지정된 평가 보고서, 중립 배지, 임베드 카드 및 인용을 공유하세요. Skillstore는 근거를 보고하지만 이 Skill의 안전 여부를 판단하지 않습니다.

버전별 보고서 열기

보고서 링크 복사

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

Markdown 배지

[![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 배지

<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>

임베드 카드

<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>

학술 인용 (APA · BibTeX · CFF)

APA 인용

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 인용

@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 점수

이 점수의 이유 증거 신뢰도: 중간

아키텍처

유지보수성

콘텐츠

커뮤니티

사양 준수

만들 수 있는 것

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.

이 프롬프트를 사용해 보세요

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.

모범 사례

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.

피하기

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.

자주 묻는 질문

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.