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Flipkart_Grid_5.0_InfoSec

Compliance Monitoring and Enforcement through Log Analysis using Large Language Models

Project Overview

As Flipkart deals with increasing volumes of data and complex systems, ensuring compliance with security policies, standards, and baselines has become a critical challenge. To address this issue, we propose a project focused on developing a system that leverages large language models for compliance monitoring and enforcement through log analysis from relevant sources.

Problem Statement

The objective is to build a solution that can effectively analyze logs, system configurations, access controls, and user privileges to check for compliance with security policies and standards. By utilizing the power of large language models (LLMs) like ChatGPT or its open-source alternatives, we aim to automate the process of identifying non-compliant activities and generating actionable insights for remediation.

Table of Contents

  1. Project Overview
  2. Getting Started
  3. Usage
  4. Features
  5. Contributing
  6. License

Getting Started

Prerequisites

Before you can run this system, make sure you have the following prerequisites installed:

  • Python (>= 3.10)
  • Node (>= 18v)
  • Dependencies listed in requirements.txt

Installation

  1. Clone the repository to your local machine:

    git clone https://github.com/darshan8850/Flipkart_Grid_5.0_InfoSec.git

  2. Install required libraries and dependencies in seperate python env (prefernce - CONDA)

    pip install -r requirements.txt

  3. Traverse to client (cd client)

    npm install

  4. Run react app

    npm run start

  5. Traverse to main directory

    python System_generated_Logs/scripts/LLM/main_server.py

Configuration

Before running the system, you need to configure it to work with your specific environment. The configuration can include defining log sources, security policies, and other parameters. Modify the configuration files in the config/ directory to match your setup.

Features

  • Automated compliance monitoring and enforcement.
  • Log analysis from various sources. (System and Human Generated)
  • Customizable configuration for different environments.
  • Actionable insights and alerts for non-compliance.
  • Integration with large language models for natural language understanding.

Demo

Project demo can be accessed by this link https://drive.google.com/drive/folders/1qH5T3qeO1hrMQ4oSrFR7s3L4soDY1ZJt?usp=drive_link

License

This project is licensed under the MIT License.