Shield AI🛡️

📚 Table of Contents

• 🧠 Overview
• 🎯 Problem Statement
• ✨ Features
• 🧩 Architecture
• 🐳 Data Utilization & AI
• 🏗️ Modules
• 🎥 Demo
• 🚀 Getting Started
  • Prerequisites
  • Setup
  • Run the App
• 🐲 Safety & Ethics
• 📂 Directory Structure
• 🧠 Future Improvements
• 📸 Snapshots
• 📚 References

🧠 Overview

Shield AI is an AI-powered cybersecurity system prototype designed to assist security analysts and IT teams. It enables faster, more intelligent threat detection and response through a user-friendly interface and AI-driven insights. This project aims to enhance security posture by leveraging Genkit and Large Language Models.

🎯 Problem Statement

Modern cyber threats are increasing in volume and sophistication, often overwhelming security teams. Shield AI addresses this by:

• Providing AI-assisted analysis of threat data.
• Offering intelligent response suggestions for security incidents.
• Centralizing threat information and logs for efficient management.
• Reducing manual effort and time-to-response for security analysts.

✨ Features

• 📊 Interactive Dashboard: Visualizes system status, active threats, security scores, and trends.
• 💧 Data Lake (Threat Intelligence Aggregation): Central hub for users to input and get AI-powered summaries, risk assessments, and recommendations from aggregated cybersecurity data.
• 🤖 Real-time Defense (AI-Powered): Enables review of detected anomalies and uses AI (Genkit flow) to generate comprehensive response strategies.
• ⚙️ Alert Configuration: Allows users to define custom thresholds and settings for triggering security alerts.
• 🛡️ Data Protection: Implements secure protocols for handling sensitive cybersecurity data. (Note: Not GDPR-compliant yet).
• 📜 Historical Threat Logs: Maintains a manageable log of resolved and ongoing risks with notes, facilitating quick diagnosis for recurrences.

🧩 Architecture

Shield AI follows modern web application architecture:

1. Frontend (User Interface):
   • Built with Next.js (App Router), React, TypeScript.
   • Styled with ShadCN UI components and Tailwind CSS.
   • Hosted on Firebase App Hosting.
   • Key Pages: Dashboard, Data Lake, Real-time Defense, Alert Configuration, Threat Logs.
2. Backend Logic (Next.js Server):
   • Utilizes Next.js Server Components and Server Actions for server-side rendering and data mutations.
   • Handles API requests from the frontend.
3. AI Orchestration (Genkit):
   • Manages AI workflows defined in src/ai/flows/.
   • Interfaces with AI models for tasks like summarization and suggestion.
4. AI Models (Google Gemini):
   • Leverages large language models for natural language understanding, text generation, and structured data output.
5. Data Flow (Example: Data Lake Analysis):
   • User inputs data on Data Lake Page (UI) → Next.js Server Action → summarizeThreatData Genkit Flow → Google Gemini Model → Genkit Flow returns results → Next.js Server Action updates UI.

🐳 Data Utilization & AI

Shield AI leverages Genkit and Google Gemini for its intelligent features:

• Threat Data Summarization (summarizeThreatData flow):
  • Processes aggregated cybersecurity data (logs, alerts).
  • Generates a concise summary.
  • Assesses the risk level (low, medium, high, critical).
  • Provides actionable recommendations.
• Response Suggestion (suggestResponse flow):
  • Takes details of a detected threat/anomaly as input.
  • Suggests a detailed remediation strategy.
  • Estimates the impact of the strategy.
  • Lists required resources.
  • Proposes a communication plan.
• Threat Briefing Generation (generateThreatBriefing flow):
  • (Conceptual) Can generate comprehensive threat landscape briefings from provided data.

🏗️ Modules

Component/Module	Description
src/app/(pages)/page.tsx	Individual page components for UI (Dashboard, Data Lake, etc.)
src/components/AppLayout.tsx	Main application shell, sidebar navigation, and overall page structure.
src/ai/genkit.ts	Initializes and configures the Genkit AI framework with necessary plugins.
src/ai/flows/*.ts	Defines specific AI-powered workflows (e.g., summarizing data, suggesting responses). Each flow orchestrates calls to LLMs with defined inputs/outputs.
src/app/globals.css	Defines the global styling and ShadCN UI theme variables for the application.
next.config.ts	Configuration file for the Next.js framework.
package.json	Lists project dependencies and defines scripts for running/building the app.

🧪 Key Technologies

• Frontend: Next.js, React, TypeScript, ShadCN UI, Tailwind CSS, Recharts, Lucide React
• Backend/Server Logic: Next.js (Server Actions, Server Components)
• AI Orchestration: Genkit
• AI Models: Google Gemini (via @genkit-ai/googleai)
• Hosting: Firebase App Hosting
• Styling/UI: Inter Font, Minimalist Icons (Lucide)
• Theme Colors:
  • Primary: Deep Blue (#3F51B5)
  • Background: Light Gray (#ECEFF1)
  • Accent: Teal (#009688)

🎥 Demo

A short demo video to guide through Shield AI:

[(shieldai_pikachu)](https:

🚀 Getting Started

Prerequisites

• Node.js (v18 or newer recommended)
• npm or yarn
• Firebase CLI (for deployment, optional for local dev)

Setup

1. Clone the repository (if applicable) or use the Firebase Studio environment.
2. Install dependencies:

   npm install

   or

   yarn install

3. Environment Variables:
   • Create a .env file in the root of the project.
   • Add any necessary environment variables, especially GOOGLE_API_KEY for Genkit to access Google AI Studio models:

     GOOGLE_API_KEY=your_google_ai_studio_api_key

4. Initialize Genkit (if running locally for development without Firebase emulators for Genkit):
   • Genkit flows are typically started with a development server. Check package.json for scripts like genkit:dev or genkit:watch.
   • Run in a separate terminal:

     npm run genkit:dev
     

Run the App

npm run dev

The application should now be running, typically on http://localhost:9002.

🐲 Safety & Ethics

• AI as Assistant: AI-generated suggestions are intended for review and validation by security professionals, not as fully autonomous actions in the current prototype.
• Data Security: Focus on secure handling of sensitive data (though GDPR compliance is a future goal).
• User Control: Users maintain control over configurations and final actions based on AI recommendations.

📂 Directory Structure

shield-ai/
├── src/
│   ├── app/                  # Next.js App Router: Pages and layouts
│   │   ├── (page-name)/page.tsx # Individual page components
│   │   ├── layout.tsx        # Root layout
│   │   └── globals.css       # Global styles and ShadCN theme
│   ├── components/           # Reusable React components
│   │   ├── ui/               # ShadCN UI pre-built components
│   │   └── AppLayout.tsx     # Main application shell/sidebar
│   ├── ai/                   # Genkit AI related files
│   │   ├── flows/            # Genkit flow definitions (e.g., summarize-threat-data.ts)
│   │   ├── genkit.ts         # Genkit initialization
│   │   └── dev.ts            # Genkit development server entry point
│   ├── lib/                  # Utility functions (e.g., cn.ts)
│   └── hooks/                # Custom React hooks (e.g., use-toast.ts)
├── public/                   # Static assets
├── .env                      # Environment variables (Gitignored)
├── next.config.ts            # Next.js configuration
├── tailwind.config.ts        # Tailwind CSS configuration
├── tsconfig.json             # TypeScript configuration
├── package.json              # Project dependencies and scripts
└── README.md                 # This file

🧠 Future Improvements

• Advanced automated response actions (with strict human oversight).
• Deeper integration with SIEM/SOAR tools.
• Achieve GDPR compliance for data handling.
• More sophisticated and customizable data visualizations.
• Enhanced AI memory for contextual understanding across sessions.
• Support for more data sources and automated ingestion.

📸 Snapshots

Shield AI provides a clean, modern web interface for interacting with its cybersecurity features. Key views include:

• Dashboard: Overview of security posture with charts and key metrics.
• Data Lake: Interface for inputting raw threat data and receiving AI-driven analysis.
• Real-time Defense: Page to review anomalies and get AI-suggested response plans.
• Alert Configuration: Forms to customize alert thresholds.
• Threat Logs: Table-based view for managing historical incident logs. The UI uses a consistent theme based on deep blue, light gray, and teal, with a focus on clarity and ease of use.

📚 References

This project builds upon a wide array of open-source tools, models, and libraries.

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🧪 Case Study 3: Enhancing Cybersecurity with Incremental Technologies

• Quantum Sentinel – GPT-based security alert/log analysis

  KOSASIH. Quantum Sentinel.
  GitHub Repository

• RAG for Cybersecurity – Retrieve + generate cybersecurity insights from documents

  Santos Omar. RAG for Cybersecurity Knowledge Retrieval.
  GitHub Repository

• LLMFuzzer – LLM-based fuzzer for vulnerability discovery

  mnns. LLMFuzzer: Fuzzing with LLMs.
  GitHub Repository

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🎯 AI-Enhanced Phishing Detection

• AI-Enhanced Phishing Detection

  melisasvr. AI-Enhanced Phishing Detection.
  GitHub Repository

• AI Enhanced Phishing Detection

  nagasaigudiwada. AI Enhanced Phishing Detection.
  GitHub Repository

• Phishing URL Detector

  ajaymahar. Phishing URL Detector.
  GitHub Repository

• Phishing URL Detector

  AnshGoyal-Tech. Phishing URL Detector.
  [GitHub Repository]) (https://github.com/AnshGoyal-Tech/Phishing-URL-Detector

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📚 Research: IoT Security & Anomaly Detection

• Anomaly-Based Intrusion Detection for IoT

  rhish9h. IoT Security – Anomaly-Based Intrusion Detection System.
  GitHub Repository

• Research Paper: Intrusion Detection System Based on Anomaly

  sumododda. Intrusion Detection System (Final Paper).
  PDF

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🛡️ Cybersecurity Intrusion Detection

• IDS-CICIDS2017 – Detecting suspicious activity, attacks, and malware using ML

  mohitwildbeast. IDS using CICIDS2017 Dataset.
  GitHub Repository

• Simple IDS – A basic intrusion detection system

  jashobeam. Simple IDS.
  GitHub Repository

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