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
79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
---
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name: analyzing-azure-activity-logs-for-threats
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description: 'Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative
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operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in
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Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
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'
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- analyzing
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- azure
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- activity
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- logs
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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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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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---
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# Analyzing Azure Activity Logs for Threats
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## When to Use
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- When investigating security incidents that require analyzing azure activity logs for threats
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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 security operations 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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Use azure-monitor-query to execute KQL queries against Azure Log Analytics workspaces,
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detecting suspicious admin operations and sign-in anomalies.
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```python
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from azure.identity import DefaultAzureCredential
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from azure.monitor.query import LogsQueryClient
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from datetime import timedelta
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credential = DefaultAzureCredential()
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client = LogsQueryClient(credential)
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response = client.query_workspace(
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workspace_id="WORKSPACE_ID",
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query="AzureActivity | where OperationNameValue has 'MICROSOFT.AUTHORIZATION/ROLEASSIGNMENTS/WRITE' | take 10",
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timespan=timedelta(hours=24),
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)
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```
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Key detection queries:
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1. Role assignment changes (privilege escalation)
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2. Resource group and subscription modifications
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3. Key vault secret access from new IPs
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4. Network security group rule changes
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5. Conditional access policy modifications
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## Examples
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```python
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# Detect new Global Admin role assignments
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query = '''
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AuditLogs
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| where OperationName == "Add member to role"
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| where TargetResources[0].modifiedProperties[0].newValue has "Global Administrator"
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'''
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```
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