Amex GBT’s CISO Builds An AI-Driven Threat Detection Engine That Slashes Response Latency

Adversaries are relentlessly weaponizing AI to launch machine-to-machine attacks, forcing CISOs to counter automated and unpredictable threats in real time. David Levin, CISO at American Express Global Business Travel (AMEX GBT), embeds AI into his security strategy to proactively neutralize threats before they escalate.

Core Components of the AI-Driven Strategy:

Instant Threat Interception: AI continuously scans for attack patterns, reducing response times from hours to minutes.
Amplified Analyst Impact: AI filters false positives, allowing analysts to focus on critical threats.
Comprehensive AI Risk Governance: NIST’s Risk Management Framework governs AI deployment, minimizing vulnerabilities.
Strategic Business Alignment: AI-driven security aligns with business objectives, ensuring operational continuity.

You Should Know: AI-Driven Threat Detection in Practice

1. Implementing AI Threat Detection with Linux Tools

AI-enhanced security relies on real-time log analysis. Use these Linux commands to simulate AI-driven threat detection:

 Monitor live authentication logs for brute-force attacks 
sudo tail -f /var/log/auth.log | grep "Failed password"

Use AI-powered anomaly detection with Wazuh (Open-Source SIEM) 
sudo wazuh-agentd -c /var/ossec/etc/ossec.conf

Analyze network traffic for suspicious patterns with Suricata 
sudo suricata -c /etc/suricata/suricata.yaml -i eth0

2. Automating Threat Response with Python

AI systems automate responses to detected threats. Below is a Python script using `scikit-learn` to classify malicious activity:

from sklearn.ensemble import RandomForestClassifier 
import pandas as pd

Sample dataset (features: request_frequency, IP_reputation, payload_size) 
data = pd.read_csv("threat_data.csv") 
X = data.drop("is_malicious", axis=1) 
y = data["is_malicious"]

Train AI model 
model = RandomForestClassifier() 
model.fit(X, y)

Predict threats in real time 
new_request = [[100, 0.2, 1500]] 
prediction = model.predict(new_request) 
print("AI Threat Prediction:", "Malicious" if prediction[bash] else "Safe")

3. Windows Command Line for Threat Hunting

AI-driven security extends to Windows environments. Use PowerShell for proactive threat hunting:

 Detect unusual process behavior 
Get-Process | Where-Object { $_.CPU -gt 90 } | Format-Table Name, CPU

Scan for suspicious registry modifications 
Get-ItemProperty -Path "HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\Run"

Enable Defender AI-based protection 
Set-MpPreference -EnableNetworkProtection Enabled

What Undercode Says

AI is revolutionizing cybersecurity by enabling real-time threat detection and automated responses. Key takeaways:
– Linux Admins: Deploy AI-powered tools like Wazuh and Suricata for log analysis.
– Windows Security Teams: Leverage PowerShell and Defender for AI-driven threat hunting.
– Developers: Integrate machine learning models (e.g., RandomForest) into SOC workflows.
– CISOs: Adopt NIST’s framework to govern AI risks and eliminate shadow IT threats.

AI isn’t optional—it’s the new frontline in cyber defense.

Expected Output:

A detailed guide on AI-driven threat detection, including Linux/Windows commands, Python scripts, and strategic insights for cybersecurity professionals.

Reference: VentureBeat – AMEX GBT’s AI Security Strategy