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analisando-padroes-de-acesso-a-armazenamento-em-nuvem

Detecte acessos anormais no AWS S3, GCS e Azure Blob Storage analisando CloudTrail Data Events, logs de auditoria do GCS e logs de análise do Armazenamento do Azure para identificar downloads em massa fora do expediente, acessos de novos IPs e picos de chamadas de API (ex.: GetObject) por meio de linhas de base estatísticas e detecção de anomalias em séries temporais. Use ao investigar suspeitas de exfiltração de dados na nuvem ou ao construir regras de detecção relacionadas.

O que é analisando-padroes-de-acesso-a-armazenamento-em-nuvem?

Esta habilidade capacita engenheiros de segurança e agentes de IA a detectar anomalias de acesso a armazenamento em nuvem em AWS S3, Google Cloud Storage (GCS) e Azure Blob Storage. Ao ingerir e analisar CloudTrail Data Events, logs de auditoria do GCS e registros de análise do Azure, a ferramenta constrói uma linha de base do comportamento normal de acesso — como volume horário de requisições, contagens de objetos por usuário e histórico de IPs de origem. Em seguida, são aplicadas técnicas de detecção de anomalias em séries temporais para identificar desvios como: acessos fora do horário comercial (8h–18h hora local), downloads em massa (mais de 100 chamadas GetObject por uma única entidade em 1 hora), novos IPs de origem não vistos nos últimos 30 dias e picos de enumeração de buckets (indicador de reconhecimento). A saída é um relatório de achados priorizados, ideal para alimentar playbooks de resposta a incidentes, criar regras de detecção (ex.: SIEM) ou validar a cobertura de monitoramento de segurança contra técnicas de exfiltração como Transferência de Dados para Conta Não Autorizada. Funciona em todas as principais plataformas de nuvem, exigindo Python 3.8+ com as dependências listadas.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-cloud-storage-access-patterns
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Documentação

Analisando Padrões de Acesso a Armazenamento em Nuvem

Quando Usar

  • Ao investigar incidentes de segurança que exigem análise de padrões de acesso a armazenamento em nuvem
  • Ao construir regras de detecção ou consultas de caça a ameaças para este domínio
  • Quando analistas de SOC precisam de procedimentos estruturados para este tipo de análise
  • Ao validar a cobertura de monitoramento de segurança para técnicas de ataque relacionadas

Pré-requisitos

  • Familiaridade com conceitos e ferramentas de segurança em nuvem
  • Acesso a um ambiente de teste ou laboratório para execução segura
  • Python 3.8+ com as dependências necessárias instaladas
  • Autorização apropriada para quaisquer atividades de teste

Instruções

  1. Instale as dependências: pip install boto3 requests
  2. Consulte o CloudTrail para Eventos de Dados S3 usando AWS CLI ou boto3.
  3. Construa linhas de base de acesso: volume horário de requisições, contagens de objetos por usuário, histórico de IPs de origem.
  4. Detecte anomalias:
    • Acessos fora do expediente (fora do horário 8h-18h local)
    • Downloads em massa: >100 chamadas GetObject de uma única entidade em 1 hora
    • Novos IPs de origem não vistos nos últimos 30 dias
    • Picos de enumeração ListBucket (indicador de reconhecimento)
  5. Gere um relatório de achados priorizados.
python scripts/agent.py --bucket meu-dado-sensivel --hours-back 24 --output relatorio_acesso_s3.json

Exemplos

Evento de Dados S3 do CloudTrail

{"eventName": "GetObject", "requestParameters": {"bucketName": "dados-sensiveis", "key": "financeiro/q4.xlsx"},
 "sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analista"}}

Individual skills in this repo

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

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

analyzing-docker-container-forensics

Investigate compromised Docker containers by analyzing images, layers,

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