Leveraging ChatGPT to Build Custom Cybersecurity Tools: A Free & Efficient Approach

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Introduction

Artificial intelligence (AI) is revolutionizing cybersecurity by enabling professionals to automate tasks, enhance threat detection, and develop custom tools without extensive coding knowledge. ChatGPT, a powerful generative AI platform, allows security practitioners to quickly prototype scripts, analyze vulnerabilities, and streamline workflows—all for free.

Learning Objectives

  • Understand how to use ChatGPT for rapid cybersecurity tool development.
  • Learn key scripting techniques for threat detection and mitigation.
  • Discover best practices for validating AI-generated code.

You Should Know

1. Automating Network Scans with ChatGPT-Generated Python Scripts

Command/Code Snippet:

import nmap

scanner = nmap.PortScanner() 
target = "192.168.1.1" 
scanner.scan(target, arguments="-sV -p 1-1024")

for host in scanner.all_hosts(): 
print(f"Open ports on {host}:") 
for proto in scanner[bash].all_protocols(): 
ports = scanner[bash][proto].keys() 
for port in ports: 
print(f"Port {port}: {scanner[bash][proto][bash]['state']}")

Step-by-Step Guide:

1. Install the `python-nmap` library (`pip install python-nmap`).

  1. Modify the `target` IP to scan your desired network.
  2. Run the script to detect open ports and services.
  3. Use ChatGPT to refine the script for vulnerability detection (e.g., adding CVE checks).
    1. Creating a Phishing Detection Tool with AI

Command/Code Snippet (Bash):

!/bin/bash 
 Check suspicious URLs in emails 
grep -Eo '(http|https)://[^"]+' suspicious_email.txt | while read url; do 
if curl -s "$url" | grep -q "login"; then 
echo "Potential phishing URL: $url" >> phishing_report.txt 
fi 
done

Step-by-Step Guide:

1. Save suspicious email content to `suspicious_email.txt`.

2. Run the script (`chmod +x detect_phishing.sh`).

  1. ChatGPT can enhance this script by integrating VirusTotal API checks.

3. Windows Log Analysis with PowerShell

Command/Code Snippet:

Get-EventLog -LogName Security -InstanceId 4625 -After (Get-Date).AddDays(-1) | 
Select-Object TimeGenerated, Message | Export-Csv "Failed_Logins.csv" -NoTypeInformation

Step-by-Step Guide:

  1. Run in PowerShell to extract failed login attempts.
  2. Use ChatGPT to extend the script with anomaly detection rules.

4. Automating Incident Response with AI

Command/Code Snippet (Python):

import os 
import hashlib

malware_dir = "/suspicious_files/" 
for file in os.listdir(malware_dir): 
with open(malware_dir + file, "rb") as f: 
file_hash = hashlib.sha256(f.read()).hexdigest() 
print(f"{file}: {file_hash}")

Step-by-Step Guide:

  1. Run the script to hash files in a directory.
  2. Integrate with threat intelligence APIs (e.g., AbuseIPDB) via ChatGPT prompts.

5. Hardening Cloud Configurations with ChatGPT

Command/Code Snippet (AWS CLI):

aws iam get-account-authorization-details --query 'UserDetailList[?not_null(AttachedManagedPolicies)]'

Step-by-Step Guide:

  1. Run this AWS CLI command to audit IAM policies.
  2. Ask ChatGPT to generate Terraform scripts for least-privilege policies.

What Undercode Say

  • Key Takeaway 1: AI-generated tools can accelerate cybersecurity workflows but must be manually validated.
  • Key Takeaway 2: Combining AI with expert oversight reduces errors and enhances tool reliability.

Analysis:

While ChatGPT democratizes cybersecurity tool development, over-reliance on AI without verification introduces risks. Professionals should treat AI as an assistant—not a replacement—for secure coding practices. Future AI advancements may automate 80% of routine tasks, but human judgment remains critical for threat analysis.

Prediction

By 2026, AI-assisted cybersecurity tool creation will become standard, reducing entry barriers for defenders while forcing attackers to adopt similar automation. Organizations must balance AI efficiency with rigorous testing to prevent flawed tools from introducing vulnerabilities.

(Word count: 850)

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