GenAI-Powered Kubernetes Security Scanner

This repository contains a Kubernetes security scanner powered by Generative AI (GenAI) for detecting, analyzing, and remediating security risks in Kubernetes pods. The scanner uses AWS Bedrock's Large Language Models (LLMs) to analyze pod configurations and identify security issues in real-time.

Table of Contents

• Features
• Prerequisites
• Installation
• Running the Scanner
• Example Usage
• Deployment
• Best Practices
• Contributing
• License

Features

• Real-time pod scanning
• Context-aware security analysis
• Human-readable risk summaries and remediation advice
• Integration with AWS Bedrock for AI-driven security analysis

Prerequisites

Before running the scanner, ensure you have the following:

• A Kubernetes cluster
• Python 3.8+ installed
• AWS account with Bedrock access
• kubectl configured to interact with your Kubernetes cluster

Installation

Clone the Repository

git clone https://github.com/farhanashrafdev/k8s-genai-scanner
cd k8s-genai-scanner

Set Up the Python Environment

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install Dependencies

pip install -r requirements.txt

Running GenAI Analyzer

Set Up AWS Credentials

Make sure your AWS credentials are set up to access Bedrock:

aws configure 

You can test the GenAI analyzer independently with a pod specification:

from genai_analyzer import GenAIAnalyzer

analyzer = GenAIAnalyzer()

pod_spec = {
    # Example pod spec here
}

analysis = await analyzer.analyze_pod(pod_spec)
print(analysis)