detecting-azure-lateral-movement
by mukul975detecting-azure-lateral-movement helps security analysts hunt lateral movement in Azure AD/Entra ID and Microsoft Sentinel using Microsoft Graph audit logs, sign-in telemetry, and KQL correlation. Use it for incident triage, detection engineering, and security audit workflows covering consent abuse, service principal misuse, token theft, and cross-tenant pivoting.
This skill scores 78/100, which means it is a solid listing candidate for directory users who need Azure/Entra ID lateral-movement hunting support. The repository provides real detection content, a usable trigger context, and enough operational material to reduce guesswork versus a generic prompt, though the install page should still note some implementation gaps.
- Explicit use cases and when-to-use guidance for incident response, threat hunting, and monitoring coverage validation.
- Concrete workflow evidence: Microsoft Graph audit/sign-in endpoints plus Sentinel KQL detections for consent grants, service principal abuse, and token replay.
- Support script and API reference indicate the skill is intended for executable hunting, not just narrative guidance.
- Prerequisites are present but the excerpt shows at least one truncated/unclear requirement line, so setup may need extra verification before use.
- No install command and limited visible progressive-disclosure signals mean users may need to inspect the script/reference files to understand exact execution steps.
Overview of detecting-azure-lateral-movement skill
detecting-azure-lateral-movement is a cybersecurity skill for hunting lateral movement in Azure AD/Entra ID and Microsoft Sentinel environments. It helps analysts turn rough incident questions into practical detections built around Microsoft Graph audit data, sign-in logs, and KQL correlation. If you need detecting-azure-lateral-movement for Security Audit, the core job is to identify suspicious identity abuse early: consent grants, service principal misuse, token replay, cross-tenant pivoting, and related privilege escalation paths.
What this skill is best for
Use this skill when you are building detections, triaging identity incidents, or validating coverage against cloud-first attacker techniques. It is a good fit for SOC analysts, threat hunters, and security engineers who want a focused detecting-azure-lateral-movement guide instead of a generic Azure security prompt.
What makes it different
The skill is not just about querying logs. It is oriented around correlating multiple Azure sources and mapping those signals to realistic attack paths. That matters because lateral movement in Entra ID often leaves weak, distributed traces rather than one obvious event.
When it is a poor fit
If your goal is general Azure hardening, IAM design, or non-security admin tasks, this is the wrong tool. It is also not a replacement for a full incident response process or a mature detection engineering platform.
How to Use detecting-azure-lateral-movement skill
Install and inspect the skill files
Use the repository path and load the skill with:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-azure-lateral-movement
For the detecting-azure-lateral-movement install decision, the most useful files are skills/detecting-azure-lateral-movement/SKILL.md, references/api-reference.md, and scripts/agent.py. Read the reference file first if you want endpoint and KQL context; read the script first if you want to understand the execution flow and indicator logic.
Feed it the right input
The skill works best when you provide a concrete hunting goal, not a vague request. Strong input includes tenant context, log sources available, time window, and the suspicious behavior you want to test.
Good prompt shape:
- “Investigate possible OAuth consent abuse in a Microsoft 365 tenant using Sentinel KQL and Graph audit logs for the last 7 days.”
- “Build detections for cross-tenant sign-in anomalies and token replay in Entra ID, assuming I have SigninLogs and AuditLogs.”
- “Create a step-by-step hunt for service principal credential additions tied to privilege escalation.”
Use the workflow that matches your environment
Start from the attack pattern, then map it to available telemetry, then refine to a query or investigative sequence. If you only have Microsoft Sentinel, lean on KQL. If you can query Microsoft Graph directly, use that for directory audits and service principal checks, then correlate findings back to sign-in behavior. That sequence matters more than broad prompt length.
What to check first in the repo
Prioritize these repository areas in order:
SKILL.mdfor the detection scope and prerequisites.references/api-reference.mdfor Graph endpoints and sample KQL.scripts/agent.pyfor indicator names, query flow, and assumptions about telemetry.
This order helps you avoid missing dependencies like permissions, log retention, or API access before you try to run a hunt.
detecting-azure-lateral-movement skill FAQ
Is this only for Microsoft Sentinel users?
No. Sentinel is the main analysis surface, but the skill also supports Microsoft Graph-driven investigation. If you can export or query AuditLogs and SigninLogs, you can still use the methodology.
Do I need advanced Azure expertise?
Not necessarily. The detecting-azure-lateral-movement skill is beginner-friendly for analysts who already know basic identity logging concepts. You do need enough context to distinguish app consent, service principal activity, and sign-in anomalies.
How is this different from a normal prompt?
A normal prompt may generate a generic Azure hunting query. This skill is more opinionated: it points you toward specific identity abuse patterns, useful telemetry, and a practical analysis path. That usually reduces false starts and improves detecting-azure-lateral-movement usage quality.
When should I not use it?
Do not use it for broad compliance reporting, tenant inventory work, or unrelated endpoint malware investigations. It is most valuable when the suspected behavior involves identity pivoting, delegated access abuse, or cloud-native lateral movement.
How to Improve detecting-azure-lateral-movement skill
Give the model sharper telemetry context
Better inputs produce better hunts. Specify which logs you have, whether they are complete or sampled, and the period you need to search. For example, “I have 30 days of AuditLogs, 14 days of SigninLogs, and no identity protection feed” is much more actionable than “check for bad activity.”
Anchor the request to one attacker path
Pick one hypothesis per run: consent grant abuse, service principal credential change, token replay, mailbox delegation, or cross-tenant pivoting. Mixing too many paths in one request usually produces shallow detections and weak prioritization.
Ask for outputs that are operationally useful
The best results are usually investigation steps, KQL, and triage guidance, not just a summary. Ask for fields to inspect, expected benign explanations, and the next query to run after the first hit. That makes detecting-azure-lateral-movement for Security Audit far more usable in real work.
Iterate after the first draft
Use the first output to find gaps: missing permissions, unsupported tables, or overly broad filters. Then tighten the request with tenant-specific details, known legitimate apps, or a narrower time window. That is the fastest way to turn the detecting-azure-lateral-movement skill into a repeatable hunt rather than a one-off answer.