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Mahipal
2fb6a9faff
Each rewritten description now states both what the skill does (concrete capability, named tools/artifacts) and an explicit when-to-use trigger, improving agent discovery/activation. Grounded in each skill's own body; changes confined to the `description` field only (bodies and all other frontmatter untouched). Produced by a gated audit->rewrite->recheck loop (548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded). Schema: 817/817 pass. Framework-ID gate: 0 defects.
78 lines
2.3 KiB
Markdown
78 lines
2.3 KiB
Markdown
---
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name: analyzing-cloud-storage-access-patterns
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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.
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domain: cybersecurity
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subdomain: cloud-security
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tags:
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- cloud-security
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- aws-s3
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- gcs
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- azure-blob-storage
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- cloudtrail
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- data-access-anomaly
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- exfiltration-detection
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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atlas_techniques:
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- AML.T0024
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- AML.T0056
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nist_ai_rmf:
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- MEASURE-2.7
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- MAP-5.1
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- MANAGE-2.4
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nist_csf:
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- PR.IR-01
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- ID.AM-08
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- GV.SC-06
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- DE.CM-01
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mitre_attack:
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- T1530
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- T1567.002
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- T1619
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- T1078.004
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- T1048
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---
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# Analyzing Cloud Storage Access Patterns
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## When to Use
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- When investigating security incidents that require analyzing cloud storage access patterns
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with cloud security concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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1. Install dependencies: `pip install boto3 requests`
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2. Query CloudTrail for S3 Data Events using AWS CLI or boto3.
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3. Build access baselines: hourly request volume, per-user object counts, source IP history.
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4. Detect anomalies:
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- After-hours access (outside 8am-6pm local time)
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- Bulk downloads: >100 GetObject calls from single principal in 1 hour
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- New source IPs not seen in the prior 30 days
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- ListBucket enumeration spikes (reconnaissance indicator)
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5. Generate prioritized findings report.
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```bash
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python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
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```
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## Examples
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### CloudTrail S3 Data Event
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```json
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{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
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"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
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```
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