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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondetecting-azure-lateral-movementExecute the skills CLI command in your project's root directory to begin installation:
Fetches detecting-azure-lateral-movement from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate detecting-azure-lateral-movement. Access via /detecting-azure-lateral-movement in your agent's command palette.
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.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
8.6K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
8.6K
stars
| 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 |
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.
Enable diagnostic settings to stream Azure AD logs to Log Analytics:
Create KQL analytics rules in Sentinel for:
Chain multiple low-confidence indicators into high-confidence lateral movement detections by correlating sign-in anomalies with directory changes within time windows.
Create Sentinel playbooks (Logic Apps) to automatically revoke suspicious OAuth grants, disable compromised service principals, and enforce step-up authentication.
JSON report containing detected lateral movement indicators, correlated event chains, affected identities, and recommended containment actions with MITRE ATT&CK technique mappings.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
detecting-azure-lateral-movement reduced setup friction for our internal harness; good balance of opinion and flexibility.
detecting-azure-lateral-movement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: detecting-azure-lateral-movement is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: detecting-azure-lateral-movement is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added detecting-azure-lateral-movement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in detecting-azure-lateral-movement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: detecting-azure-lateral-movement is focused, and the summary matches what you get after install.
I recommend detecting-azure-lateral-movement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in detecting-azure-lateral-movement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 29