mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
71 lines
2.2 KiB
Markdown
71 lines
2.2 KiB
Markdown
---
|
|
name: analyzing-cloud-storage-access-patterns
|
|
description: Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS
|
|
audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API
|
|
calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
|
|
domain: cybersecurity
|
|
subdomain: cloud-security
|
|
tags:
|
|
- analyzing
|
|
- cloud
|
|
- storage
|
|
- access
|
|
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
|
|
---
|
|
|
|
|
|
# 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
|
|
|
|
1. Install dependencies: `pip install boto3 requests`
|
|
2. Query CloudTrail for S3 Data Events using AWS CLI or boto3.
|
|
3. Build access baselines: hourly request volume, per-user object counts, source IP history.
|
|
4. 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)
|
|
5. Generate prioritized findings report.
|
|
|
|
```bash
|
|
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
|
|
```
|
|
|
|
## Examples
|
|
|
|
### CloudTrail S3 Data Event
|
|
```json
|
|
{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
|
|
"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
|
|
```
|