SENTINEL X: An AI-Powered Cybersecurity Dashboard for Threat Detection

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

SENTINEL X is an AI-assisted cybersecurity dashboard designed to help security learners, small businesses, and security teams explore suspicious activity, detect anomalies, assess risks, and understand incident-response workflows from one interactive interface.

Small organizations may not have dedicated security operations teams or the budget for expensive enterprise security platforms. SENTINEL X explores how an accessible, AI-assisted tool could make cybersecurity monitoring concepts easier to understand and investigate.

Key Features

• Threat Monitor: Explore simulated security events and suspicious activity.
• ML-Based Anomaly Detection: Use an Isolation Forest model to identify unusual patterns in telemetry.
• Risk Analysis: Review risk scores and security insights.
• Response Center: Explore recommended responses and simulated audit logs.
• DEVIL AI Assistant: Get AI-assisted explanations when the relevant model integration is configured.
• Interactive Dashboard: Explore cybersecurity information through a Streamlit interface.

Who Could Pay for It?

The potential customers include small businesses, cybersecurity training providers, IT consultants, and educational institutions looking for accessible security analytics and training tools.

A future commercial version could offer subscription-based analytics, custom telemetry integrations, team dashboards, and reporting. These are potential future offerings, not claims about features already implemented.

Important: The current project uses simulated telemetry and simulated response actions. It is an educational prototype, not a production intrusion-detection system or an automated containment platform.

Demo

Live Demo: [https://sentinel-x-raghuraj.streamlit.app/]

If the cloud deployment is not ready yet, you can share a short screen-recording demo instead.

Code

GitHub Repository: SENTINEL X

The repository contains the project source code and supporting files. Explore the implementation, review the dashboard, and suggest improvements.

How I Built It

I built SENTINEL X using Python and Streamlit, with machine-learning and data-processing libraries to create an interactive cybersecurity simulation.

Technology Stack

• Python — application logic and data processing
• Streamlit — interactive dashboard
• Pandas and NumPy — telemetry and numerical operations
• Scikit-learn — Isolation Forest anomaly detection
• Ollama with a local language model — optional AI-assisted explanations when configured

The dashboard brings threat exploration, anomaly detection, risk analysis, and response workflows together. The local language-model integration is optional, so a cloud deployment needs an appropriate configuration for any features that depend on it.

Why Does Open Innovation Matter?

Open innovation makes it possible for students, independent developers, and security enthusiasts to learn from existing tools and build on shared knowledge rather than starting from scratch.

For SENTINEL X, open-source libraries make machine learning, data processing, and dashboard development accessible without requiring a large proprietary software stack.

Using an open-weight language model through a local inference tool such as Ollama can also offer more control over model selection and experimentation than relying exclusively on a closed hosted AI API. Local inference does require suitable hardware and configuration, and it is not automatically available on a hosted Streamlit deployment.

Keeping the project open source gives other developers an opportunity to inspect the implementation, contribute improvements, experiment with detection approaches, and help make cybersecurity education more accessible.

My Agent Session

Optional: Add a DevRelay agent-session recording or link here if you have one. Otherwise, remove this section.

Prize Categories

Add only the partner prize categories for which the project genuinely qualifies, according to the challenge rules.

---

Built as an educational project exploring AI-assisted cybersecurity, anomaly detection, and open-source innovation.