Listen to this Post
Introduction:
The modern Security Operations Center (SOC) is no longer just a wall of SIEM screens; it is a fusion of real-time endpoint detection, big data analytics, and generative AI. As highlighted by a senior SOC professional currently navigating the Paris market, the demand has shifted toward engineers who can navigate the entire stack—from traditional firewall rules to training deep learning models for anomaly detection. This article dissects the essential toolkit required for next-generation SOC roles, focusing on the convergence of EDR/SIEM orchestration and local AI model deployment.
Learning Objectives:
- Integrate EDR tools like Cortex XDR with SIEM platforms such as Splunk for advanced correlation.
- Deploy and optimize local AI models using PyTorch for cybersecurity detection use cases.
- Master the command-line tools necessary for managing Zabbix monitoring and Grafana dashboards.
- Understand the shift from traditional playbooks to AI-driven incident response.
You Should Know:
- Converging EDR and SIEM: The Cortex XDR and Splunk Pipeline
Modern SOCs utilize EDR (Endpoint Detection and Response) for granular host visibility and SIEM for centralized log aggregation. The combination of Palo Alto Cortex XDR and Splunk is a formidable stack.
Step‑by‑step guide explaining what this does and how to use it:
To forward Cortex XDR alerts to Splunk for long-term storage and advanced correlation, you typically use the Cortex XDR API and a Splunk Heavy Forwarder.
Linux (Splunk Heavy Forwarder Setup):
Download and install Splunk Heavy Forwarder (example for Ubuntu) wget -O splunkforwarder.deb "https://download.splunk.com/products/universalforwarder/releases/9.2.1/linux/splunkforwarder-9.2.1-78803f08a587-linux-2.6-amd64.deb" sudo dpkg -i splunkforwarder.deb Navigate to the Splunk bin directory cd /opt/splunkforwarder/bin Create a script to hit the Cortex XDR API and ingest data sudo nano /opt/splunkforwarder/bin/get_cortex_alerts.sh
Inside the script:
!/bin/bash
API_KEY="YOUR_CORTEX_API_KEY"
API_SECRET="YOUR_API_SECRET"
FQDN="https://api-xxx.xdr.us.paloaltonetworks.com"
Get recent incidents
curl -X POST "$FQDN/public_api/v1/incidents/get_incidents/" \
-H "x-xdr-auth-id: $API_KEY" \
-H "Authorization: $API_SECRET" \
-H "Content-Type: application/json" \
-d '{"request_data": {"filters": [{"field": "creation_time", "operator": "gte", "value": "2026-03-01T00:00:00Z"}]}}' \
| /opt/splunkforwarder/bin/splunk add oneshot - -sourcetype "cortex:xdr:alert" -index "main"
Make it executable and add to crontab to run every 5 minutes.
Windows (PowerShell equivalent for Cortex ingestion):
Define API endpoint
$Uri = "https://api-xxx.xdr.us.paloaltonetworks.com/public_api/v1/xql/start_xql_query/"
$Headers = @{
"x-xdr-auth-id" = "YOUR_API_KEY"
"Authorization" = "YOUR_API_SECRET"
"Content-Type" = "application/json"
}
$Body = @{
"request_data" = @{
"query" = "dataset = xdr_data | fields alert_name, alert_id, agent_hostname | limit 10"
}
} | ConvertTo-Json
Execute query and pipe to Splunk HTTP Event Collector (HEC)
Invoke-RestMethod -Uri $Uri -Method Post -Headers $Headers -Body $Body |
ConvertTo-Json |
Invoke-WebRequest -Uri "https://your-splunk-hec:8088/services/collector" `
-Method Post -Headers @{"Authorization"= "Splunk HEC_TOKEN"}
- Local AI and Anomaly Detection: PyTorch for Cybersecurity
The profile mentions proficiency in PyTorch. Using AI locally allows SOC analysts to run models without sending sensitive data to the cloud. A practical application is detecting anomalies in network flow data.
Step‑by‑step guide explaining what this does and how to use it:
This guide demonstrates setting up a basic autoencoder in PyTorch to detect anomalous network traffic patterns.
Linux Environment Setup:
Create a virtual environment python3 -m venv ai_soc_env source ai_soc_env/bin/activate Install PyTorch (CPU version for demo) pip install torch torchvision numpy pandas matplotlib
Python Script (autoencoder.py):
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
Sample: Simulated NetFlow data (bytes, packets, duration)
data = np.random.rand(1000, 10) 100
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
Convert to tensor
data_tensor = torch.tensor(data_scaled, dtype=torch.float32)
Define Autoencoder
class Autoencoder(nn.Module):
def <strong>init</strong>(self):
super().<strong>init</strong>()
self.encoder = nn.Sequential(
nn.Linear(10, 7),
nn.ReLU(),
nn.Linear(7, 4)
)
self.decoder = nn.Sequential(
nn.Linear(4, 7),
nn.ReLU(),
nn.Linear(7, 10)
)
def forward(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
Train
epochs = 100
for epoch in range(epochs):
optimizer.zero_grad()
outputs = model(data_tensor)
loss = criterion(outputs, data_tensor)
loss.backward()
optimizer.step()
if epoch % 20 == 0:
print(f'Epoch {epoch}, Loss: {loss.item():.4f}')
Detection: If reconstruction loss is high, it's anomalous
reconstructions = model(data_tensor)
losses = torch.mean((reconstructions - data_tensor) 2, dim=1)
threshold = 1.5 Typically set via percentile on validation data
anomalies = losses > threshold
print(f"Number of anomalies detected: {sum(anomalies).item()}")
3. Infrastructure Monitoring: Zabbix and Grafana Integration
Zabbix provides robust network monitoring, while Grafana offers superior visualization. This combination allows SOCs to spot infrastructure anomalies that might indicate a breach (e.g., sudden CPU spikes on a domain controller).
Step‑by‑step guide explaining what this does and how to use it:
Connecting Zabbix to Grafana to visualize security metrics.
Linux (Install Grafana and Zabbix Plugin):
Add Grafana repository sudo apt-get install -y software-properties-common sudo add-apt-repository "deb https://packages.grafana.com/oss/deb stable main" wget -q -O - https://packages.grafana.com/gpg.key | sudo apt-key add - sudo apt-get update sudo apt-get install grafana Install Zabbix plugin sudo grafana-cli plugins install alexanderzobnin-zabbix-app sudo systemctl restart grafana-server
Configuration in Grafana Web UI (accessible via http://localhost:3000):
1. Enable the Zabbix plugin in Configuration > Plugins.
2. Add a Zabbix data source.
- Set URL to your Zabbix API endpoint (e.g., `http://your-zabbix-server/api_jsonrpc.php`).
4. Use Zabbix credentials.
5. Build dashboards for “Security-Relevant Metrics” like ICMP ping loss, disk I/O on critical servers, or specific Zabbix triggers for “Failed Login Attempts.”
4. SIEM Querying Mastery: Splunk SPL and KQL
Effective threat hunting relies on query languages. A certified Splunk professional must be adept at Search Processing Language (SPL).
Step‑by‑step guide explaining what this does and how to use it:
Hunting for Pass-the-Hash attacks using Splunk.
Windows Event Logs indexed in Splunk:
index=windows sourcetype=WinEventLog:Security EventCode=4624 | search Logon_Type=3 | stats count by Account_Name, Source_Network_Address, ComputerName | where count > 5 | join type=inner Account_Name [search index=windows sourcetype=WinEventLog:Security EventCode=4624 Logon_Type=9 | stats values(ProcessName) by Account_Name] | table Account_Name, Source_Network_Address, ComputerName, count
Explanation:
– Filters for successful network logons (type 3).
– Groups by account and source IP.
– Counts occurrences; if an account logs in from many IPs rapidly, it flags.
– Joins with Logon Type 9 (same account using different credentials) to identify potential pass-the-hash.5. Hardening Cloud EDR: MDE (Microsoft Defender for Endpoint)
Microsoft Defender for Endpoint (MDE) is critical for hybrid environments. Hardening involves configuring advanced features via PowerShell.Step‑by‑step guide explaining what this does and how to use it:
Enabling attack surface reduction rules via PowerShell for all domain-joined machines.Windows PowerShell (Run as Administrator):
Connect to MDE (requires module) Install-Module -Name MicrosoftDefenderATP -Force $Session = New-MdeApiSession -ClientId "your-app-id" -ClientSecret "your-secret" Deploy Attack Surface Reduction Rules $Rules = @( @{Id = "9e6c4e1f-7d60-4723-b288-0e3c2c7794d2"; Action = "Enabled"} Block Office apps from creating child processes @{Id = "3b576869-a4ec-4529-8536-b80a7769e899"; Action = "Enabled"} Block executable content from email client ) foreach ($Rule in $Rules) { $Body = @{ ruleId = $Rule.Id action = $Rule.Action target = "AuditMode" Or "Warn", "Block" } | ConvertTo-Json Invoke-RestMethod -Uri "https://api.security.microsoft.com/api/asr/rules" ` -Method Post -Body $Body -Headers @{Authorization = "Bearer $($Session.AccessToken)"; "Content-Type" = "application/json"} } Check status on a local machine Get-MpPreference | Select-Object asr
What Undercode Say:
- Key Takeaway 1: The modern SOC engineer is a hybrid role requiring equal parts deep infrastructure knowledge (Linux/Windows CLI) and cutting-edge data science (PyTorch). The profile of Mathieu Pichon exemplifies the need to master both legacy tools (Cisco ASA) and modern AI stacks.
- Key Takeaway 2: Automation is no longer optional. Integrating EDR APIs directly into SIEM pipelines, as demonstrated with Cortex and Splunk, allows for proactive hunting rather than reactive alerting. The use of local AI for anomaly detection ensures data privacy and real-time response, critical for zero-trust architectures.
- Key Takeaway 3: The skills gap is widening. The mention of management alongside deep technical skills indicates that leadership values professionals who can bridge the gap between board-level risk (GRC) and technical implementation (XSOAR playbooks). Continuous learning, as evidenced by the pursuit of multiple certifications, is the only sustainable defense.
Prediction:
By 2027, the “Junior AI” role will become a standard tier within SOCs. We will see the commoditization of local LLMs for log analysis, drastically reducing the Mean Time to Respond (MTTR). However, this will also create a new attack surface—adversarial AI targeting model training data. The demand for professionals who understand both the mathematics of applied linear algebra and the syntax of firewall rules will outstrip supply, forcing organizations to invest heavily in upskilling programs rather than just hiring. The integration of cryptocurrency skills within security roles suggests a future where digital asset protection is as fundamental as network perimeter defense.
▶️ Related Video (82% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Mathieu Pichon – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
