Cybersecurity & Information Security

Sigma Rules: Writing, Testing, and SIEM Conversion

6 min readPublished: August 5, 2026

Professional visual illustration on writing Sigma Rules in Threat Hunting and Detection

Quick answer

Writing Sigma Rules begins with a question or behavior to identify, continues with defining Telemetry and logic, and concludes with testing, tuning, documentation, and controlled deployment. Quality is measured by coverage and investigative capability.

Threat Hunting and Detection Engineering transform knowledge about adversary behavior into measurable questions, data sources, and detection rules. The goal is not to generate more alerts, but to improve coverage and decision quality. This article focuses on writing Sigma Rules and is intended for SOC analysts and beginner Detection engineers. The aim is to provide a working methodology that can be applied in practice, professional interviews, and work environments, without merely relying on a dictionary definition.

The main challenge is that data is almost always partial. title/id/status, logsource, detection selections can point to a direction, but their meaning depends on time, asset, user, and expected activity. Therefore, we will build the test around an investigation question, required evidence, and a clear criterion for completion.

The practical scenario in the article is: writing a rule for anomalous Process Creation in the lab. All examples are lab data or process descriptions. When it comes to Penetration Testing, Web, or Cloud, one must work only with explicit permission, defined scope, and the ability to stop the test.

Sigma Structure

The important fields are not necessarily those displayed at the top of the screen. When writing Sigma Rules, one must identify stable identifiers, time, source, destination, outcome, and context. Useful examples include title/id/status, logsource, detection selections, condition, falsepositives, tags. The goal is to enable correlation between records and not just reading a single Event.

It is recommended to create a small data dictionary: field name, meaning, format, source, expected Null values, and whether it is reliable for linking. This allows distinguishing between a display field and an investigative identifier, and identifying when a Connector or version has changed the Schema.

Logsource and Fields

Selection and Condition

The topic 'Selection and Condition' is a central part of working on writing Sigma Rules. It is recommended to break it down into three questions: what is the input, what decision do we want to make, and what evidence is sufficient to justify it. These questions prevent automatic use of the tool without understanding the goal.

In practice, record the title/id/status, logsource, detection selections, condition, falsepositives, compare to expected behavior, and define at least one Pivot. The result should be testable by another analyst, including limitations and next steps.

False Positives and Level

Improving Sigma Rule writing must start with a Baseline. Measure volume, rate of useful cases, investigation time, missing sources, and reason for closure. A change that reduces Alerts but hides real activity is not a success.

Tuning options include threshold, time window, targeted allowlist, asset context, suppression, and an exception based on an approved process. Every exception needs an owner, validity period, and cancellation conditions. After the change, run a Test corpus and compare before/after.

Conversion and Testing in SIEM

Professional testing for writing Sigma Rules begins with success and failure conditions. Define a positive case, a negative case, a boundary case, and similar legitimate activity. This allows identifying both False Negatives and False Positives.

In an authorized environment, use minimal action that proves the claim without causing damage. Save Input, Output, time, and version, and after correction, perform a Retest in the same scenario and also check for Regression on nearby functions.

Unique Testing Focus Areas

In this topic, it is recommended to build a focused evidence map in advance. The main testing focus areas are: title/id/status, logsource, detection selections, condition, falsepositives, tags. The list is not an automatic checklist; each item is chosen because it can link an entity, action, and time or explain legitimate behavior.

title/id/status: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.
logsource: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.
detection selections: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.
condition: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.
falsepositives: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.
tags: Define the expected value, what would be considered anomalous, and what additional source would verify the finding.

When one of the focus areas is unavailable, the gap should be documented and an alternative chosen. For example, if a Process identifier is unstable, one can use time, Host, User, and Parent; if Payload is encrypted, use Metadata, volume, frequency, and TLS/DNS context.

Recommended Workflow

Define a Scope and one working question regarding writing Sigma Rules.
Record the data sources and required evidence: title/id/status, logsource, detection selections, condition.
Create a short Baseline of normal behavior or expected outcome.
Perform the minimal test in a lab environment and save time, input, and output.
Build a Timeline or comparison table and separate fact from interpretation.
Perform a Pivot to an additional source to confirm or refute the initial explanation.
Summarize the decision, limitations, recommended action, and Retest criterion.

Practical Scenario

The chosen scenario is writing a rule for anomalous Process Creation in the lab. The purpose of the exercise is not to prove attack capability, but to practice collection, comparison, and documentation safely. Before starting work, define mock data, a time window, and an expected outcome.

At the end of the exercise, a product should be submitted that another analyst or tester can review: a screenshot or export of the evidence, a short Timeline, an initial assumption, corroborating evidence, a limitation, and a recommendation. When there is insufficient evidence, the correct conclusion is that the scenario was not proven.

Step	What to perform	Output
Preparation	Define Scope, time, and target. Record which fields or evidence from title/id/status, logsource, detection selections are expected to appear.	Short test plan
Data Creation	Perform a safe and simulated action related to writing Sigma Rules, without real information or impact on a production system.	Controlled event/request/flow
Collection	Collect the raw evidence and context from another source. Verify Time zone, identifiers, and integrity.	Two linked pieces of evidence
Analysis	Write what each piece of evidence proves, what it does not prove, and what is the possible legitimate explanation.	Interim conclusion
Conclusion	Choose closure, escalation, finding, or tuning; add recommendation and Retest.	Documented output

Practical Checklist

Check and document: Hypothesis.
Check and document: ATT&CK technique.
Check and document: Data sources.
Check and document: Detection logic.
Check and document: Expected benign behavior.
Check and document: Test cases and coverage.
Note Time zone, tool version, and collection time.
Save raw data before filtering or modification.
Write what the finding proves and what is still unknown.
Define owner and next action with a deadline.

Common Mistakes

Starting from a random IOC without a Hypothesis.
Mapping ATT&CK by name only.
Writing a Rule without Test cases.
Ignoring legitimate behavior.
Measuring Rules instead of Coverage.
Not managing versions.

Summary and CTA

Sigma Rules: Writing, Testing, and SIEM Conversion is a topic that connects technical knowledge with work discipline. Start with a question, collect only relevant evidence, maintain context and time, and choose an action that can be justified and re-tested.

In HPI's Cybersecurity & AI track, these principles are practiced using systems, logs, and labs. A natural next step is to move on to the linked articles, perform the lab exercise, and save the output as part of a professional portfolio.

FAQ

Does writing Sigma Rules alone prove an attack or weakness?

No. It provides a signal or finding that requires context, validation, and an additional source. A professional conclusion relies on a sequence of evidence and alignment with expected behavior.

What to do when some data is missing?

Document the missing data, check for an alternative source, and reduce the confidence level. Do not fill in fields speculatively or present 'Unknown' as normal.

How long should evidence be kept?

The time depends on policy, regulation, cost, and event type. It is important to pre-define Retention, Legal hold, and the ability to export evidence in a verifiable format.

How to practice without risking a real system?

Use virtual machines, mock data, CTF, or a dedicated lab. In authorized tests, define Scope, Stop conditions, and backup before starting work.