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Introduction:
As artificial intelligence reshuffle boardroom strategies, cybersecurity teams face a dual challenge: harnessing AI to improve threat detection while grounding executive expectations in reality. The concept of “Act like a Detection Engineer” has emerged as a mindset that combines hands-on technical skills with analytical rigor, moving beyond hype to build resilient defense mechanisms. This article unpacks the core principles of detection engineering, provides actionable labs and commands, and explores how AI can both aid and mislead security operations.
Learning Objectives
- Understand the role and responsibilities of a detection engineer in modern SOC environments.
- Learn to create, test, and refine detection rules using open-source tools and AI assistance.
- Identify common pitfalls where AI overpromises and underdelivers in security analytics.
You Should Know
- What Is Detection Engineering? – Building Your First Lab
Detection engineering focuses on developing, maintaining, and improving the rules and algorithms that alert security teams to malicious activity. It bridges threat intelligence, data analysis, and incident response.
Step‑by‑step guide to setting up a detection lab with Elastic Stack:
- Install Elasticsearch and Kibana on a Linux machine (Ubuntu 20.04+):
wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add - sudo apt-get install apt-transport-https echo "deb https://artifacts.elastic.co/packages/8.x/apt stable main" | sudo tee /etc/apt/sources.list.d/elastic-8.x.list sudo apt-get update && sudo apt-get install elasticsearch kibana
2. Start the services:
sudo systemctl start elasticsearch sudo systemctl start kibana
- Install and configure Winlogbeat on a Windows endpoint to ship logs:
– Download Winlogbeat from Elastic’s website.
– Edit `winlogbeat.yml` to set the Elasticsearch output: output.elasticsearch.hosts: ["http://your-linux-ip:9200"].
– Install and start the service:
.\install-service-winlogbeat.ps1 Start-Service winlogbeat
- Verify data ingestion in Kibana by creating an index pattern for
winlogbeat-.
Now you have a mini-SIEM to develop and test detections.
2. Leveraging AI for Detection Rule Generation
AI models like GPT can accelerate the creation of detection logic, but they must be used with caution. Here’s how to generate a Sigma rule for suspicious PowerShell activity using the OpenAI API.
Python script to request a Sigma rule:
import openai openai.api_key = "your-api-key" prompt = """ Generate a Sigma rule that detects PowerShell executing base64 encoded commands. Include logsource (category: process_creation, product: windows) and a relevant detection section. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=300 ) print(response.choices[bash].text.strip())
Example output (manually verified):
title: PowerShell Base64 Encoded Command logsource: category: process_creation product: windows detection: selection: Image|endswith: '\powershell.exe' CommandLine|contains: '-EncodedCommand' condition: selection
Important: Always test AI‑generated rules in a lab—false positives are common.
3. Hands‑On: Building a Detection Rule from Scratch
Let’s create a detection for a real technique: Remote Desktop Protocol (RDP) logins from unauthorized IPs using Windows Event ID 4624 (logon type 10).
Step 1: Query Event Logs with PowerShell
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4624} |
Where-Object { $<em>.Properties[bash].Value -eq 10 } |
Select-Object TimeCreated, @{Name='SourceIP'; Expression={$</em>.Properties[bash].Value}}
Step 2: Convert to a Reusable Detection (Splunk Search)
index=windows EventCode=4624 LogonType=10 | stats count by SourceIP, AccountName | where not match(SourceIP,"10.0.0..")
Step 3: Implement in Elastic SIEM as a custom query
event.code:4624 and winlog.event_data.LogonType:10 and not winlog.event_data.IpAddress:10.0.0.
Add this as a custom rule in the Kibana “Security” app and set an appropriate severity.
4. Testing Your Detections with Atomic Red Team
Red Canary’s Atomic Red Team provides small, repeatable tests for each MITRE ATT&CK technique.
Install Atomic Red Team on Windows:
IEX (IWR 'https://raw.githubusercontent.com/redcanaryco/invoke-atomicredteam/master/install-atomicredteam.ps1' -UseBasicParsing) Install-AtomicRedTeam -getAtomics
Execute a test for PowerShell base64 command:
Invoke-AtomicTest T1059.001 -TestNumbers 1
Monitor your SIEM to see if your rule triggers. Adjust thresholds or logic based on results.
- Avoiding the CEO Delusion: AI Hype vs. Reality in Security
While AI can summarise logs and draft rules, it does not replace domain expertise. Many CEOs fall for the myth that AI will solve all security problems. As a detection engineer, you must:
- Validate AI output with adversarial thinking.
- Understand data pipelines – garbage in, garbage out.
- Communicate effectively with leadership about what AI can and cannot do.
A practical step: present a comparison table of AI‑generated rules vs. handcrafted ones during team meetings to ground discussions.
6. Continuous Learning: Certifications and Resources
Tony Moukbel’s profile lists 57 certifications – a testament to the breadth of knowledge required. For aspiring detection engineers, focus on:
- SANS SEC555: SIEM with Tactical Analytics
- GIAC Continuous Monitoring Certification (GMON)
- Elastic Certified Engineer
- Cloud security certifications (AWS Security Specialty, Azure Security Engineer)
Online labs like CyberDefenders.org and DetectionLab (https://github.com/clong/DetectionLab) offer hands-on practice.
7. Future of Detection Engineering: AI-Augmented, Human-Led
The role will evolve from writing individual rules to designing machine learning pipelines and feedback loops. Expect:
- Increased use of Jupyter notebooks for data exploration.
- Integration of LLMs in analyst workflows for triage.
- Demand for engineers who can explain model drift to non‑technical stakeholders.
Stay ahead by learning Python data libraries (pandas, scikit‑learn) and contributing to open‑source detection projects like Sigma or Elastic’s detection-rules.
What Undercode Say
- Detection engineering is a craft, not a commodity – AI can assist, but the core skill lies in understanding attacker behavior, data sources, and the art of writing precise logic that minimises noise.
- Executive education is part of the job – Without clear communication, security teams risk being overruled by AI‑fuelled boardroom fantasies. Ground discussions with metrics and proof‑of‑concept demos.
- Hands‑on labs remain irreplaceable – Simulating attacks and tuning detections in a controlled environment builds the intuition that no textbook or AI can provide.
Prediction
Within the next two years, we will see the emergence of “Detection Engineering as Code” platforms where teams version‑control rules, run automated tests, and deploy them via CI/CD pipelines. AI will automate the initial triage of alerts, but false positive rates will initially spike, forcing organisations to invest more in skilled engineers who can fine‑tune the models. The gap between AI‑enabled SOCs and those still using legacy methods will widen, making continuous upskilling the only sustainable strategy.
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