Files
Anthropic-Cybersecurity-Skills/skills/analyzing-azure-activity-logs-for-threats/SKILL.md
T
mukul975 efca3ec611 feat: add NIST CSF 2.0 nist_csf field to all 754 cybersecurity skills
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)
2026-04-06 11:17:40 +02:00

2.3 KiB

name, description, domain, subdomain, tags, version, author, license, nist_csf
name description domain subdomain tags version author license nist_csf
analyzing-azure-activity-logs-for-threats Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections. cybersecurity security-operations
analyzing
azure
activity
logs
1.0 mahipal Apache-2.0
DE.CM-01
RS.MA-01
GV.OV-01
DE.AE-02

Analyzing Azure Activity Logs for Threats

When to Use

  • When investigating security incidents that require analyzing azure activity logs for threats
  • 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 security operations 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

Use azure-monitor-query to execute KQL queries against Azure Log Analytics workspaces, detecting suspicious admin operations and sign-in anomalies.

from azure.identity import DefaultAzureCredential
from azure.monitor.query import LogsQueryClient
from datetime import timedelta

credential = DefaultAzureCredential()
client = LogsQueryClient(credential)

response = client.query_workspace(
    workspace_id="WORKSPACE_ID",
    query="AzureActivity | where OperationNameValue has 'MICROSOFT.AUTHORIZATION/ROLEASSIGNMENTS/WRITE' | take 10",
    timespan=timedelta(hours=24),
)

Key detection queries:

  1. Role assignment changes (privilege escalation)
  2. Resource group and subscription modifications
  3. Key vault secret access from new IPs
  4. Network security group rule changes
  5. Conditional access policy modifications

Examples

# Detect new Global Admin role assignments
query = '''
AuditLogs
| where OperationName == "Add member to role"
| where TargetResources[0].modifiedProperties[0].newValue has "Global Administrator"
'''