NullCon Goa 2026: Mastering AI-Driven Threat Detection – A Step-by-Step Lab Guide + Video

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Introduction

At NullCon Goa 2026, the message was clear: artificial intelligence is no longer a futuristic concept in cybersecurity—it is the present battleground. Discussions moved beyond buzzwords to practical implementations of AI-driven threat landscapes, detection engineering, and signature evolution. This article translates those insights into a hands-on guide, enabling you to build a homelab, craft intelligent detection rules, and align your skills with the industry’s rapid shift toward automation and machine learning.

Learning Objectives

  • Understand how AI and machine learning are reshaping threat detection and response.
  • Build a fully functional AI-powered security homelab using open-source tools.
  • Write and test Sigma and YARA rules with machine learning enhancements.
  • Implement anomaly detection models on real security data.
  • Optimize your professional profile for AI-focused cybersecurity roles.

You Should Know:

1. Setting Up an AI-Powered Threat Detection Homelab

A solid homelab is the foundation for experimenting with AI-driven security. We’ll use the Elastic Stack (Elasticsearch, Logstash, Kibana) because it offers built‑in machine learning features and a free tier perfect for learning.

Step‑by‑step guide (Ubuntu 22.04 LTS):

1. Update system and install dependencies:

sudo apt update && sudo apt upgrade -y
sudo apt install openjdk-11-jdk apt-transport-https wget curl -y

2. Add Elastic GPG key and repository:

wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -
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 update

3. Install Elasticsearch, Logstash, and Kibana:

sudo apt install elasticsearch logstash kibana -y

4. Start and enable services:

sudo systemctl enable --now elasticsearch
sudo systemctl enable --now logstash
sudo systemctl enable --now kibana
  1. Configure Kibana to bind to all interfaces (for homelab only):
    Edit `/etc/kibana/kibana.yml` and set server.host: "0.0.0.0". Then restart Kibana: 

    sudo systemctl restart kibana

6. Install Elastic’s machine learning sample data:

Access Kibana at http://<your-ip>:5601, go to “Machine Learning” and add the “Sample web logs” data. This will create an index and enable you to explore pre‑built anomaly detection jobs.

  1. Ingest your own security logs (e.g., from Sysmon on Windows):
    On a Windows machine, install Sysmon with a default config, then forward logs to Logstash using Winlogbeat. Download Winlogbeat from Elastic, configure it to point to your Logstash port (usually 5044), and start the service.

Winlogbeat sample config (`winlogbeat.yml`):

output.logstash:
hosts: ["<your-linux-ip>:5044"]

2. Building Custom Detection Rules with Sigma

Sigma is a generic signature format that can be converted into queries for many SIEMs. It allows you to write rules once and deploy everywhere.

Step‑by‑step guide:

1. Install Sigma CLI on your Linux machine:

git clone https://github.com/SigmaHQ/sigma.git
cd sigma
pip install -r requirements.txt
  1. Create a Sigma rule to detect suspicious PowerShell execution:

Save the following as `powershell_suspicious.yml`:

title: Suspicious PowerShell Command Line
status: test
description: Detects PowerShell commands with common obfuscation or suspicious parameters
logsource:
product: windows
service: powershell
detection:
selection:
EventID: 4104
ScriptBlockText|contains:
- ' -enc '
- ' -e '
- ' bypass '
- ' hidden '
condition: selection
level: high
  1. Convert the Sigma rule to an Elasticsearch query: 
    cd sigma
python tools/sigmac -t es-qs -c tools/config/elasticsearch.yml -r powershell_suspicious.yml

    The output will be a JSON query you can paste into Kibana’s “Discover” or use in a detection rule.

  2. Test the rule by generating a malicious PowerShell command on a Windows endpoint (in your lab) and verifying that Kibana triggers an alert.

3. Integrating Machine Learning for Behavioral Analysis

Elastic’s ML can detect anomalies automatically, but you can also build custom models using Python.

Step‑by‑step guide:

  1. Export logs from Elasticsearch to a CSV file using a Python script: 
    from elasticsearch import Elasticsearch
import pandas as pd
es = Elasticsearch("http://localhost:9200")
resp = es.search(index=".ds-logs-", body={"query": {"match_all": {}}}, size=10000)
df = pd.json_normalize([hit["_source"] for hit in resp["hits"]["hits"]])
df.to_csv("security_logs.csv", index=False)

  2. Set up a Python environment with scikit-learn and Jupyter: 

    pip install jupyter pandas scikit-learn matplotlib
jupyter notebook

  3. Train an isolation forest model to detect outlier network traffic:
    In a new notebook, load the CSV and apply the model: 

    import pandas as pd
from sklearn.ensemble import IsolationForest
df = pd.read_csv("security_logs.csv")
Select numeric columns (e.g., bytes sent, duration)
features = df[["bytes_out", "bytes_in", "duration"]].fillna(0)
model = IsolationForest(contamination=0.05)
df["anomaly"] = model.fit_predict(features)
anomalies = df[df["anomaly"] == -1]
print(f"Detected {len(anomalies)} anomalous events")

  4. Leveraging YARA for Malware Detection with AI Enhancements
    YARA rules are essential for malware identification. Tools like `yarGen` use machine learning to automatically generate rules from malware samples.

Step‑by‑step guide:

1. Install yarGen (requires Python):

git clone https://github.com/Neo23x0/yarGen.git
cd yarGen
pip install -r requirements.txt
python yarGen.py --update
  1. Collect a few malware samples (use safe test files from MalwareBazaar) and place them in a folder, e.g., malware_samples/.

3. Run yarGen to generate rules:

python yarGen.py -m malware_samples/ -o my_malware_rules.yar
  1. Review and refine the generated rules – they are based on string statistics and ML heuristics. You can then test them with `yara` against other samples.

5. Resume Optimization for AI Security Roles

The NullCon resume clinic emphasised that interviewers scan for impact and relevance. Here’s how to tailor your resume:

  • Include homelab projects with quantifiable outcomes: “Built an AI‑driven threat detection lab using Elastic Stack and Python, reducing false positives by 30% in simulated attacks.”
  • Highlight AI/ML skills: List specific libraries (scikit‑learn, TensorFlow) and tools (Elastic ML, Jupyter).
  • Use keywords from job descriptions: “detection engineering,” “anomaly detection,” “security automation,” “threat hunting.”
  • Show continuous learning: Mention recent certifications (e.g., CompTIA Security+, CySA+, or specialized AI security courses).

6. Practical Hands-On: Simulating AI-Driven Attacks and Defense

To validate your setup, simulate an attack using MITRE Caldera or Atomic Red Team.

Step‑by‑step guide (using Atomic Red Team on Windows):

  1. On a Windows VM, install Atomic Red Team: 
    IEX (IWR 'https://raw.githubusercontent.com/redcanaryco/invoke-atomicredteam/master/install-atomicredteam.ps1' -UseBasicParsing);
Install-AtomicRedTeam -getAtomics

2. Run a test technique, e.g., T1059.001 (PowerShell):

Invoke-AtomicTest T1059.001 -TestNumbers 1
  1. Observe the alerts in Kibana. If you’ve enabled ML jobs, look for anomaly scores spiking during the test. Adjust your rules based on the results.

7. Future-Proofing Your Skills: Continuous Learning Paths

  • Courses: SANS SEC599 (Defeating Advanced Adversaries), Coursera’s “AI for Cybersecurity” specialization.
  • Certifications: (ISC)² CCSP, GIAC Open Source Intelligence (GOSI), or vendor‑specific ones like Elastic Certified Engineer.
  • Homelab challenges: Participate in platforms like CyberDefenders or HackTheBox that offer AI‑based challenges.

What Undercode Say

  • Key Takeaway 1: AI is not a magic wand; it demands clean, labeled data and skilled engineers who can tune models. The real value lies in combining human intuition with machine speed.
  • Key Takeaway 2: Hands‑on experimentation—building a homelab, writing rules, training models—is irreplaceable. The professionals who will thrive are those who can bridge the gap between theory and practice.

NullCon 2026 underscored that the security industry is at an inflection point. Automation is accelerating both attacks and defenses. To stay ahead, one must not only understand AI concepts but also be able to implement them. The discussions around detection engineering and signature evolution highlighted a move away from static signatures toward adaptive, behavior‑based systems. This shift will force organisations to invest heavily in data pipelines, machine learning operations, and cross‑functional teams. The informal homelab conversations at the conference proved that the community is already embracing this change, sharing scripts, configurations, and war stories.

Prediction:

Over the next three years, AI will become as fundamental to security operations as firewalls are today. We will see the emergence of autonomous response systems that can contain threats in real time, but also the rise of AI‑powered adversarial techniques—deepfake phishing, automated vulnerability discovery, and polymorphic malware. The demand for security engineers who can build, tune, and defend AI systems will skyrocket, and those who start building their skills now will lead the next wave of cyber defence.

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