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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
82 lines
2.3 KiB
Markdown
82 lines
2.3 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
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by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics.
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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
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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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- 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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