detecting-azure-lateral-movement

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-azure-lateral-movement

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Installation Guide

How to use detecting-azure-lateral-movement on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add detecting-azure-lateral-movement
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-azure-lateral-movement

Fetches detecting-azure-lateral-movement from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-azure-lateral-movement

Restart Cursor to activate detecting-azure-lateral-movement. Access via /detecting-azure-lateral-movement in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

name
detecting-azure-lateral-movement
description
Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.
domain
cybersecurity
subdomain
cloud-security
tags
- azure - entra-id - lateral-movement - sentinel - kql - graph-api - cloud-security - threat-hunting
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- PR.IR-01 - ID.AM-08 - GV.SC-06 - DE.CM-01

Detecting Azure Lateral Movement

Overview

Lateral movement in Azure AD/Entra ID differs from on-premises environments. Attackers pivot through OAuth application consent grants, service principal abuse, cross-tenant access policies, and stolen refresh tokens rather than SMB/RDP connections. Detection requires correlating Microsoft Graph API audit logs, Azure AD sign-in logs, and Entra ID protection risk events using KQL queries in Microsoft Sentinel. This skill covers building detection analytics for common Azure lateral movement techniques including application impersonation, mailbox delegation abuse, and conditional access policy bypasses.

When to Use

  • When investigating security incidents that require detecting azure lateral movement
  • 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

  • Azure subscription with Microsoft Sentinel workspace configured
  • Azure AD P2 or Entra ID P2 license for risk-based sign-in detection
  • Microsoft Graph API permissions: AuditLog.Read.All, Directory.Read.All, SecurityEvents.Read.All
  • Log Analytics workspace ingesting AuditLogs, SigninLogs, and AADServicePrincipalSignInLogs
  • Familiarity with KQL (Kusto Query Language)

Steps

Step 1: Configure Log Ingestion

Enable diagnostic settings to stream Azure AD logs to Log Analytics:

  • Sign-in logs (interactive and non-interactive)
  • Audit logs (directory changes, app consent)
  • Service principal sign-in logs
  • Provisioning logs
  • Risky users and risk detections

Step 2: Build Detection Queries

Create KQL analytics rules in Sentinel for:

  • Unusual service principal credential additions
  • OAuth application consent grants to unknown apps
  • Cross-tenant sign-ins from new tenants
  • Token replay from different IP/user-agent combinations
  • Mailbox delegation changes (FullAccess, SendAs)

Step 3: Correlate Events

Chain multiple low-confidence indicators into high-confidence lateral movement detections by correlating sign-in anomalies with directory changes within time windows.

Step 4: Automate Response

Create Sentinel playbooks (Logic Apps) to automatically revoke suspicious OAuth grants, disable compromised service principals, and enforce step-up authentication.

Expected Output

JSON report containing detected lateral movement indicators, correlated event chains, affected identities, and recommended containment actions with MITRE ATT&CK technique mappings.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.729 reviews
  • A
    Anaya HuangDec 16, 2024

    detecting-azure-lateral-movement reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • B
    Benjamin ThomasDec 4, 2024

    detecting-azure-lateral-movement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • B
    Benjamin BansalNov 23, 2024

    Keeps context tight: detecting-azure-lateral-movement is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • A
    Amelia LiNov 7, 2024

    We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • A
    Anika DialloOct 26, 2024

    Keeps context tight: detecting-azure-lateral-movement is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • D
    Dev RaoOct 14, 2024

    We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • D
    Dev KimSep 25, 2024

    Useful defaults in detecting-azure-lateral-movement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • S
    Sakshi PatilSep 21, 2024

    Solid pick for teams standardizing on skills: detecting-azure-lateral-movement is focused, and the summary matches what you get after install.

  • C
    Chaitanya PatilAug 12, 2024

    I recommend detecting-azure-lateral-movement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • R
    Rahul SantraJul 23, 2024

    Useful defaults in detecting-azure-lateral-movement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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