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Cloudsplaining으로 AWS IAM 정책의 위험한 권한 감사

에이전트가 AWS 정책 변경이 프로덕션에 도달하기 전에 권한 상승 경로와 과도한 IAM 권한을 플래그해야 할 때 Cloudsplaining을 사용합니다.

Cloudsplaining으로 AWS IAM 정책의 위험한 권한 감사란 무엇인가요?

이 기술은 AI 에이전트가 AWS IAM 정책을 분석하여 과도한 권한과 권한 상승 경로와 같은 보안 위험을 식별할 수 있도록 Cloudsplaining을 활용합니다. 개발자에게 유용한 이유는 정책 변경이 프로덕션에 배포되기 전에 잠재적인 보안 허점을 사전에 탐지할 수 있기 때문입니다. Claude Code, Cursor, Codex 등 주요 AI 에이전트 플랫폼에서 사용할 수 있으며, AWS 계정 데이터나 정책 JSON 파일을 입력으로 받아 분석합니다. Cloudsplaining은 iam:GetAccountAuthorizationDetails API를 사용하여 계정 세부 정보를 가져오고, EC2, ECS, EKS, Lambda와 같은 AWS 컴퓨팅 서비스가 가정할 수 있는 IAM 역할을 식별하며, 사용자 정의 예외 파일을 지원하여 오탐지를 필터링합니다. 결과는 HTML 보고서와 raw JSON 파일로 제공되어 상세한 후처리가 가능합니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/audit-aws-iam-policies-for-risky-permissions-with-cloudsplaining
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Python Version

문서

Cloudsplaining으로 AWS IAM 정책의 위험한 권한 감사은(는) 무엇을 하나요?

에이전트가 AWS 정책 변경이 프로덕션에 도달하기 전에 권한 상승 경로와 과도한 IAM 권한을 플래그해야 할 때 Cloudsplaining을 사용합니다.

사전 조건

Python 3, AWS IAM 정책 JSON 또는 계정 데이터, 그리고 Cloudsplaining.

설치

환경에 맞는 업스트림 설치 또는 설정 경로를 사용하십시오:

업스트림의 요구 사항 및 주의 사항:

  • Python Version
  • iam:GetAccountAuthorizationDetails를 실행할 권한이 있어야 합니다. arn:aws:iam::aws:policy/SecurityAudit 정책에 필요한 권한이 포함되어 있습니다...
  • default-iam-results.json: 이 파일은 보고서의 원시 JSON 출력을 포함합니다. 이 데이터 파일을 사용하여 다양한 목적으로 스캔 결과를 처리할 수 있습니다. 예를 들어, 이 파일을 구문 분석하는 Python 스크립트를 작성할 수 있습니다...

기본 사용법 또는 시작 노트:

  • 예제 보고서

  • Cloudaplaining은 AWS 컴퓨팅 서비스(예: EC2, ECS, EKS, Lambda)에서 가정할 수 있는 IAM 역할도 식별합니다. 이러한 역할은 사용자 정의 역할보다 더 큰 위험을 나타낼 수 있으며, 특히 AWS 컴퓨팅 서비스가...

  • 다양한 이유로 오탐지인 결과를 필터링하기 위해 사용자 정의 예외 파일을 지정할 수도 있습니다. 예를 들어, 사용자 정책은 설계상 허용적이지만 시스템 역할은 일반적으로 더 제한적입니다...

  • 출처: https://github.com/salesforce/cloudsplaining

  • 업스트림 문서에서 추출: https://raw.githubusercontent.com/salesforce/cloudsplaining/HEAD/README.md

문서

출처

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add executable smoke tests for shell scripts and CLIs before ref

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Add graph-backed memory and context retrieval to agent workflows

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Add persistent project memory and hook-driven synthesis with Cla

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Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

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