Microsoft Sentinel KQL: A Practical Introduction for Threat Hunting

Threat hunting goes beyond simply waiting for alerts. It involves actively testing hypotheses against telemetry to identify weak signals that automated detection systems might overlook. Microsoft Sentinel is designed for this proactive approach, but the key to effective hunting lies in KQL (Kusto Query Language). You don't need to memorize every command; rather, focus on a small set of commands, establish a repeatable workflow, and utilize a library of adaptable patterns. Here's what you'll learn:

1. Difference between threat hunting, detection, and investigation.

2. Where to hunt in Sentinel, including tables, time windows, and fields.

3. Essential KQL commands for successful hunting.

4. Reusable hunting query patterns.

5. How to interpret results accurately.

Threat hunting differs from detection and investigation in that it starts with a hypothesis and searches for traces of evidence, rather than solely responding to alerts or following an alert-driven timeline. Detection relies on analytics rules to generate alerts from known patterns, while investigation involves reconstructing the timeline after following an alert. Threat hunting, on the other hand, is a proactive method focused on finding real issues before they escalate into incidents.

Before you begin writing queries, ensure you have the necessary connectors enabled (such as Microsoft 365, Azure, Defender, and Windows), sufficient retention periods, and a consistent naming/convention for workspaces, tables, and VIP accounts. Additionally, establish a clear objective for your hunting efforts.

To discover available data in Sentinel, start with a broad global search and gradually narrow down your search by applying time-bound filters and selecting only the necessary fields. The KQL hunter's kit includes essential commands like 'where', 'project', 'extend', 'summarize', 'order by', 'take', 'join', 'parse', and 'mv-expand'. Utilize these commands to effectively filter, project, extend, summarize, correlate, and parse data.

Some common hunting query patterns include searching for abnormal authentication attempts, identifying multiple-factor authentication (MFA) and sign-in failures, performing broad searches for known indicators, spotting suspicious PowerShell execution, and correlating successful sign-ins with device logons. Once you have identified a promising pattern, analyze the results to determine who, what, where, when, and what next.

If the story behind the data cannot be clearly explained, further investigation is needed. Finally, transform your successful hunts into repeatable capabilities by developing saved queries, workbooks, or analytics rules.