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Introduction
Kusto Query Language (KQL) is a powerful tool for threat hunters, detection engineers, and incident responders working in Microsoft security environments. With Microsoft offering free on-demand instructor-led training, professionals can now sharpen their KQL skills to detect, investigate, and mitigate threats effectively.
Learning Objectives
- Understand KQL syntax and operators for log analysis.
- Apply KQL queries to hunt threats in Microsoft Defender and Sentinel.
- Develop custom detection rules using KQL for proactive security.
You Should Know
1. Basic KQL Query Structure
SecurityEvent | where EventID == 4625 // Failed logon attempts | summarize FailedAttempts = count() by Account | sort by FailedAttempts desc
Step-by-Step Guide:
1. `SecurityEvent` specifies the log source.
2. `where` filters events for failed logins (EventID 4625).
3. `summarize` counts failures per account.
4. `sort` ranks results by highest attempts.
2. Hunting Suspicious Processes
DeviceProcessEvents
| where FileName in~ ("powershell.exe", "cmd.exe")
| where InitiatingProcessFileName != "explorer.exe"
| project Timestamp, DeviceName, AccountName, FileName, CommandLine
How to Use:
- Identifies PowerShell/cmd executions not launched via Explorer.
– `project` refines output columns for analysis.
3. Detecting Lateral Movement
SecurityEvent | where EventID == 4648 // Logon with explicit credentials | where SubjectUserName != TargetUserName | summarize Attempts = count() by SubjectUserName, TargetUserName
Purpose: Flags potential pass-the-hash or credential abuse.
4. Querying Azure Activity Logs
AzureActivity | where OperationName == "Create VM" | where Caller != "[email protected]" | project Caller, ResourceGroup, VMName
Use Case: Monitors unauthorized VM creation in Azure.
5. Advanced Joins for Threat Correlation
let MaliciousIPs = datatable(IP:string) ["1.1.1.1", "2.2.2.2"]; SecurityEvent | where EventID == 4625 | join kind=inner MaliciousIPs on $left.IPAddress == $right.IP
Explanation: Cross-references failed logins with a predefined malicious IP list.
6. Time-Based Anomaly Detection
SecurityEvent | where EventID == 4688 // Process creation | summarize LateNightActivity = countif(TimeGenerated > 22:00 or TimeGenerated < 06:00) by Account | where LateNightActivity > 5
Why It Matters: Highlights unusual after-hours process execution.
7. Custom Detection Rule Template
SecurityAlert | where AlertName == "Suspicious PowerShell Execution" | extend Entities = parse_json(Entities) | mv-expand Entities | where Entities.Type == "host" | summarize Alerts = count() by Entities.Address
Actionable Output: Aggregates alerts by host for prioritization.
What Undercode Say
- Key Takeaway 1: KQLās real-time log analysis capabilities are critical for modern SOCs.
- Key Takeaway 2: Free Microsoft training democratizes access to enterprise-grade threat-hunting skills.
Analysis:
As adversaries evolve, KQL proficiency becomes a force multiplier. Microsoftās free training bridges the skill gap, enabling defenders to leverage native tools like Sentinel and Defender more effectively. The provided queries address common use casesāfrom credential theft to cloud misconfigurationsābut adaptability is key. Future-proof your career by mastering these techniques, as KQLās role in automation and AI-driven security workflows will only expand.
Prediction
KQL will become the lingua franca for cross-platform threat detection as Microsoft integrates it deeper into Azure, M365, and third-party connectors. Expect a surge in KQL-based automation, reducing manual analysis workloads by 40% in the next 3 years.
Note: Replace example IPs/emails with actual data. Always validate queries in a test environment before production use.
IT/Security Reporter URL:
Reported By: Mehmetergene On – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ā
