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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
71 lines
2.2 KiB
Markdown
71 lines
2.2 KiB
Markdown
---
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name: analyzing-cloud-storage-access-patterns
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description: Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS
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audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API
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calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
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domain: cybersecurity
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subdomain: cloud-security
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tags:
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- analyzing
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- cloud
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- storage
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- access
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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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---
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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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