Mastering KQL for Cybersecurity: Advanced Query Techniques for Microsoft Entra

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Introduction

Kusto Query Language (KQL) is a powerful tool for analyzing large datasets, particularly in cybersecurity and Microsoft Entra (formerly Azure AD). With the rise of AI-assisted scripting, understanding KQL’s core principles remains critical for validating and refining automated outputs. This article explores advanced KQL techniques for Privileged Identity & Access Management (PIAM) and provides actionable commands for security professionals.

Learning Objectives

  • Learn to construct KQL queries for detecting anomalous privileged access in Microsoft Entra.
  • Understand how to integrate KQL with AI tools for enhanced threat hunting.
  • Master practical commands for log analysis and incident response.

1. Detecting Privileged Account Anomalies with KQL

Command:

SigninLogs 
| where UserDisplayName has "admin" 
| where ResultType == "0" 
| where TimeGenerated > ago(7d) 
| summarize LoginCount = count() by UserPrincipalName, AppDisplayName 
| where LoginCount > 10

Step-by-Step Guide:

  1. Purpose: Identifies frequent privileged sign-ins over seven days.

2. Breakdown:

  • Filters `SigninLogs` for successful (ResultType == "0") admin logins.
  • Aggregates counts per user and application.
  • Flags users with >10 logins for investigation.

2. Hunting for Impossible Travel with KQL

Command:

SigninLogs 
| where ResultType == "0" 
| project UserPrincipalName, IPAddress, Location, TimeGenerated 
| sort by UserPrincipalName asc, TimeGenerated desc 
| extend TimeDiff = next(TimeGenerated) - TimeGenerated 
| where TimeDiff < 1h and Location != next(Location)

Step-by-Step Guide:

  1. Purpose: Detects logins from geographically distant locations in a short timeframe.

2. Breakdown:

  • Compares consecutive logins (next()) for the same user.
  • Triggers if locations differ within an hour (TimeDiff < 1h).

3. Auditing Conditional Access Policy Failures

Command:

AuditLogs 
| where OperationName == "Evaluate conditional access policies" 
| where Result == "failure" 
| extend FailureReason = tostring(parse_json(tostring(ResultReason)).failureReason 
| project TimeGenerated, UserPrincipalName, FailureReason

Step-by-Step Guide:

1. Purpose: Pinpoints Conditional Access policy failures.

2. Breakdown:

  • Parses JSON `ResultReason` to extract failureReason.
  • Outputs user and failure context for troubleshooting.

4. Extracting Risky User Sessions

Command:

IdentityRiskEvents 
| where RiskState == "atRisk" 
| join (IdentityInfo) on $left.UserId == $right.Id 
| project RiskEventType, UserPrincipalName, RiskLevel, LastUpdated

Step-by-Step Guide:

1. Purpose: Correlates risk events with user identities.

2. Breakdown:

  • Joins `IdentityRiskEvents` with `IdentityInfo` for user details.
  • Exports high-risk (RiskLevel == "high") sessions.

5. Automating KQL with PowerShell

Command (PowerShell):

Invoke-AzOperationalInsightsQuery -WorkspaceId "YourWorkspaceID" -Query "SigninLogs | where RiskDetail == 'aiConfirmedSigninSafe'"

Step-by-Step Guide:

  1. Purpose: Executes KQL queries via PowerShell for automation.

2. Breakdown:

  • Uses Azure module Az.OperationalInsights.
  • Queries sign-ins flagged as safe by AI (for baseline analysis).

What Undercode Say

  • Key Takeaway 1: KQL remains indispensable for validating AI-generated security scripts. While AI can draft queries, human expertise ensures precision in threat detection.
  • Key Takeaway 2: Microsoft Entra’s integration with KQL enables real-time monitoring of privileged access, a critical capability for Zero Trust architectures.

Analysis: The intersection of KQL and AI is reshaping cybersecurity workflows. However, as noted in LinkedIn discussions (e.g., Skip Hofmann’s comment), over-reliance on AI without foundational KQL knowledge risks misinterpretation of results. Future developments may see KQL embedded in low-code AI tools, but mastery of its syntax will still be required to audit and refine outputs.

Prediction: KQL will evolve into a hybrid language combining natural language processing (NLP) for AI-assisted query drafting, while retaining its procedural core for expert-level tuning. Organizations investing in KQL training today will gain a strategic advantage in cloud-native security.

For further learning, register for KQL Cafe sessions.

IT/Security Reporter URL:

Reported By: Activity 7340790011665022976 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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