Tutorial / Cram Notes
Hunting queries and analytical rules are critical components in any cybersecurity operation, and transitioning from the former to the latter is an essential skill for analysts, especially for those preparing for the SC-200 Microsoft Security Operations Analyst exam. This transition allows organizations to automate threat detection and proactively manage their security posture.
Hunting Queries:
Hunting queries are essentially advanced searches that cybersecurity analysts use to manually sift through data and identify potential threats or anomalies. These searches are often ad-hoc and based on hypotheses or trends that analysts aim to investigate.
Analytical Rules:
Analytical rules, on the other hand, are automated alerts that are triggered based on specific criteria within data sets. They are used to detect known threats or suspicious activities by continuously analyzing the data streams and generating alerts for further investigation.
Conversion Process:
To convert a hunting query into an analytical rule, begin with a well-defined hunting query that has shown potential security relevance. Look for recurring patterns of activity that you can convert into a rule to monitor continuously.
Step 1: Validate the Hunting Query
Ensure that the hunting query you’ve created returns the expected results and adds value from a security perspective. It must accurately catch anomalies without producing excessive false positives.
Step 2: Parameter Determination
Decide on the parameters that will trigger your analytical rule. Parameters could be things like the number of failed login attempts, an unusual outbound network traffic pattern, or evidence of known attack techniques.
Step 3: Create the Analytical Rule
- Go to the security solution (like Microsoft Sentinel) where you intend to create the rule.
- Navigate to the section for creating analytical rules.
- Input the logic or syntax from the validated hunting query.
- Define the rule schedule and frequency.
- Specify the threshold that will trigger an alert.
Step 4: Configure Alert Details
Configure the alert details, including severity level, tactics, techniques, and any additional context you want responders to have when they’re investigating an alert generated by this rule.
Step 5: Test the Rule
Before deploying the rule, test it to ensure it works as expected. Some advanced security solutions allow the simulation of historical data to test the performance of the rule.
Step 6: Deploy the Rule
Once confirmed that the rule performs accurately in detecting the threats, it can be deployed into a live environment.
Examples:
Below is a hypothetical example to illustrate the transition from a hunting query to an analytical rule:
Hunting Query Example:
SecurityEvent
| where TimeGenerated > ago(1d)
| where EventID == 4625
| summarize Count = count() by AccountType, Account
| where Count > 20
This hunting query looks for any account that has more than 20 failed login attempts in the last day.
Analytical Rule Example:
| Field | Value |
|---|---|
| Rule logic | Same as hunting query |
| Frequency | Run every 6 hours |
| Trigger Threshold | More than 20 failed logins |
| Severity | High if AccountType == ‘User’ |
| Alert Context | Include details of failed logins |
By setting up analytical rules based on validated hunting queries, organizations can transition to a more proactive security monitoring approach, allowing analysts to focus on the investigation and response rather than manually searching through vast amounts of data.
For those preparing for the SC-200 exam, being proficient in this process demonstrates a deep understanding of threat detection and response lifecycle, a key competency of a Microsoft Security Operations Analyst.
Practice Test with Explanation
True or False: A hunting query and an analytical rule serve the exact same purpose in a threat detection environment.
- A) True
- B) False
Answer: B) False
Explanation: Hunting queries are proactive searches for threats without predefined alerts, while analytical rules are designed to generate alerts based on specific criteria automatically.
In the context of Azure Sentinel, which of the following can be used to create an analytical rule?
- A) KQL (Kusto Query Language)
- B) SQL (Structured Query Language)
- C) JavaScript
Answer: A) KQL (Kusto Query Language)
Explanation: Azure Sentinel uses KQL for both hunting queries and analytical rules to interrogate data and create detections.
True or False: Analytical rules in Azure Sentinel can be scheduled to run at specific intervals.
- A) True
- B) False
Answer: A) True
Explanation: Analytical rules in Azure Sentinel can be scheduled to run at regular intervals to continuously monitor data for potential security threats.
When converting a hunting query to an analytical rule, which of the following components need to be defined?
- A) Trigger
- B) Action
- C) Severity
- D) Logic
Answer: C) Severity
Explanation: While creating an analytical rule, you are required to define the severity of the alert that will be raised, among other settings like the trigger and the action.
True or False: Once you create an analytical rule, you cannot modify it.
- A) True
- B) False
Answer: B) False
Explanation: Analytical rules can be edited after their creation to refine alerting logic or to adjust to evolving threat landscapes.
Multiple select: What items should be considered when converting a hunting query to an analytical rule?
- A) Frequency of data ingestion
- B) Alert threshold
- C) Historical data retention
- D) Type of actions to take when an alert is triggered
Answer: B) Alert threshold, D) Type of actions to take when an alert is triggered
Explanation: When converting a hunting query to an analytical rule, consider the alert threshold (how many times an event must occur before an alert is raised) and the actions to take when an alert is triggered.
True or False: Hunting queries typically include more context within their results than analytical rules.
- A) True
- B) False
Answer: A) True
Explanation: Hunting queries are designed to explore data and therefore may include more context in the results to assist analysts in investigation, while analytical rules focus on specific alert conditions.
When configuring an analytical rule from a hunting query, which of the following is not a required step?
- A) Define the name of the rule
- B) Select the entities to monitor
- C) Determine the data source
- D) Choose the machine learning algorithm to use
Answer: D) Choose the machine learning algorithm to use
Explanation: While creating an analytical rule from a hunting query, the focus is on the query logic, the data source, and the entities. The selection of a machine learning algorithm is not a required step in this process.
True or False: Creating an analytical rule from a hunting query requires that the hunting query has resulted in previous findings.
- A) True
- B) False
Answer: B) False
Explanation: There is no prerequisite for a hunting query to have previous findings before turning it into an analytical rule. The conversion is based on recognizing the potential of a hunting query to detect certain threat patterns regularly.
Which alert processing option is typically not available in analytical rules within Azure Sentinel?
- A) Suppression
- B) Grouping
- C) Auto-healing
- D) Automation
Answer: C) Auto-healing
Explanation: Auto-healing is not a typical alert processing or response option within Azure Sentinel’s analytical rule capabilities. Suppression, grouping, and automation are available options.
Interview Questions
What is Azure Sentinel, and what is its primary purpose?
Azure Sentinel is a cloud-based security information and event management (SIEM) service provided by Microsoft. Its primary purpose is to help security professionals identify and respond to security threats and incidents.
What is a custom analytics rule, and how can it be used in Azure Sentinel?
A custom analytics rule is a security rule that you can create and customize to monitor your Azure Sentinel environment for specific security events. You can use custom analytics rules to detect and respond to security threats that are specific to your organization.
How do you create a custom analytics rule in Azure Sentinel?
You can create a custom analytics rule in Azure Sentinel by first defining the query that will be used to detect the security event you are interested in, and then configuring the rule to trigger when that event is detected.
What is a scheduled query, and how is it used in Azure Sentinel?
A scheduled query is a query that is run automatically on a regular schedule. In Azure Sentinel, you can use scheduled queries to monitor your environment for specific security events and trigger custom analytics rules when those events are detected.
What is the syntax for a Kusto query, and how can it be used in Azure Sentinel?
Kusto is the query language used in Azure Sentinel. The syntax for a Kusto query includes keywords, operators, and functions that can be used to filter, aggregate, and transform data. Kusto queries can be used in Azure Sentinel to identify and analyze security events.
How do you test a custom analytics rule in Azure Sentinel?
You can test a custom analytics rule in Azure Sentinel by running a sample query and verifying that the rule is triggered as expected. You can also use the Query explorer in Azure Sentinel to visualize the results of your query and verify that the data matches your expectations.
What is a query result aggregation, and how can it be used in Azure Sentinel?
Query result aggregation is the process of grouping query results based on specific criteria, such as a particular field or time range. In Azure Sentinel, you can use query result aggregation to visualize and analyze security events, and to trigger custom analytics rules based on specific aggregation criteria.
How can you configure a custom analytics rule to send an alert when a security event is detected?
You can configure a custom analytics rule in Azure Sentinel to send an alert when a security event is detected by setting up an alert rule that is triggered by the custom analytics rule. You can also specify the notification channels and severity levels for the alert.
How can you use the Azure Sentinel Workspace to view and analyze security events?
You can use the Azure Sentinel Workspace to view and analyze security events by using the built-in dashboards and visualizations, such as the Overview dashboard and the Incident Management dashboard. You can also create custom dashboards and visualizations based on your specific security needs.
What are the benefits of using Azure Sentinel for security monitoring and threat detection?
The benefits of using Azure Sentinel for security monitoring and threat detection include its ability to aggregate and analyze large volumes of security data, its integration with Microsoft and third-party security solutions, and its customizable and extensible analytics and alerting capabilities.
Great post on converting a hunting query to an analytical rule! How do you handle false positives when working with threat-hunting queries?
I found this blog very useful for my SC-200 preparation. Thanks!
What kind of performance impact should I expect when converting a hunting query to an analytical rule in Microsoft Sentinel?
Can anyone explain the difference between a hunting query and an analytical rule in more detail?
This post is not detailed enough for people new to Microsoft Sentinel.
Do you need to rewrite hunting queries entirely to convert them to analytical rules?
What are some best practices when converting a hunting query to an analytical rule?
Appreciate the helpful information provided in this blog!