analyzing-cloud-storage-access-patterns
Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection
Install / Use
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-cloud-storage-access-patternsInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
Our assessment of analyzing-cloud-storage-access-patterns
analyzing-cloud-storage-access-patterns scores 85/100 on our quality scale, 323rd of 461 Security skills we index.
Its SKILL.md is 2.3 KB long, split into 6 sections with 2 code examples: moderately detailed.
With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 25 days ago, so analyzing-cloud-storage-access-patterns is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
analyzing-cloud-storage-access-patterns compared with similar skills
All 4 of these similar skills score higher than analyzing-cloud-storage-access-patterns; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| analyzing-cloud-storage-access-patterns (this skill)by mukul975 | 85 | 33.3k | 25d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.4k | 9d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.8k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 83.5k | today | MCP Server |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
Frequently asked questions
- How do I install analyzing-cloud-storage-access-patterns?
- Run
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-cloud-storage-access-patterns. The install tabs above show the steps for each supported agent. - Which AI agents does analyzing-cloud-storage-access-patterns work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is analyzing-cloud-storage-access-patterns safe to use?
- It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is analyzing-cloud-storage-access-patterns still maintained?
- The repository was last updated 25 days ago, so analyzing-cloud-storage-access-patterns is actively maintained.
Skill content
View source on GitHubname: analyzing-cloud-storage-access-patterns description: Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules. domain: cybersecurity subdomain: cloud-security tags:
- cloud-security
- aws-s3
- gcs
- azure-blob-storage
- cloudtrail
- data-access-anomaly
- exfiltration-detection version: '1.0' author: mahipal license: Apache-2.0 atlas_techniques:
- AML.T0024
- AML.T0056 nist_ai_rmf:
- MEASURE-2.7
- MAP-5.1
- MANAGE-2.4 nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01 mitre_attack:
- T1530
- T1567.002
- T1619
- T1078.004
- T1048
Analyzing Cloud Storage Access Patterns
When to Use
- When investigating security incidents that require analyzing cloud storage access patterns
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with cloud security concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
- Install dependencies:
pip install boto3 requests - Query CloudTrail for S3 Data Events using AWS CLI or boto3.
- Build access baselines: hourly request volume, per-user object counts, source IP history.
- Detect anomalies:
- After-hours access (outside 8am-6pm local time)
- Bulk downloads: >100 GetObject calls from single principal in 1 hour
- New source IPs not seen in the prior 30 days
- ListBucket enumeration spikes (reconnaissance indicator)
- Generate prioritized findings report.
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
Examples
CloudTrail S3 Data Event
{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
