detecting-azure-lateral-movement▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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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.
| 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.
How to use detecting-azure-lateral-movement on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add detecting-azure-lateral-movement
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches detecting-azure-lateral-movement from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate detecting-azure-lateral-movement. Access the skill through slash commands (e.g., /detecting-azure-lateral-movement) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★29 reviews- ★★★★★Anaya Huang· Dec 16, 2024
detecting-azure-lateral-movement reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Benjamin Thomas· Dec 4, 2024
detecting-azure-lateral-movement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Benjamin Bansal· Nov 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.
- ★★★★★Amelia Li· Nov 7, 2024
We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Anika Diallo· Oct 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.
- ★★★★★Dev Rao· Oct 14, 2024
We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dev Kim· Sep 25, 2024
Useful defaults in detecting-azure-lateral-movement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Sep 21, 2024
Solid pick for teams standardizing on skills: detecting-azure-lateral-movement is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Aug 12, 2024
I recommend detecting-azure-lateral-movement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Rahul Santra· Jul 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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