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Shadow-AI Leak Detector — Home Page AI-powered platform for detecting unauthorized AI usage and preventing sensitive data leaks.
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End-to-end sensitive data detection pipeline.
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AI security workflow for data protection.
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Analyzes prompts for privacy risks, detects sensitive information, and provides security insights before data is exposed.
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PDF Sensitive Data Analyzer Scans uploaded PDF documents to identify sensitive information, credentials, and personally identifiable data.
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Detailed results of detected sensitive data.
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Dashboard for monitoring AI security events.
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Real-time protection against AI data leaks.
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Shadow-AI Footer & Quick Links Provides quick access to project resources, documentation, GitHub, and other important platform links.
Inspiration
Every time we search, browse, click, upload, or interact online, we leave behind a digital footprint. Most people are unaware of how much information can be inferred from these digital traces. We wanted to build a project that makes this hidden digital shadow visible and understandable, while showing users why being aware of their online presence matters.
What it does
Shadow-AI uses AI to analyze a user's digital footprint and identify meaningful patterns and information that contribute to their digital shadow. It transforms complex digital activity into understandable insights, helping users become more aware of their online presence, privacy risks, and the information they leave behind.
How we built it
We built Shadow-AI as a full-stack AI-powered application, combining a user-friendly frontend with backend services and AI-based analysis. The system processes relevant digital information, applies AI to interpret the data, and presents the results through an easy-to-understand interface. We focused on making the experience simple, visual, and useful rather than presenting users with raw or complicated data.
Challenges we ran into
One of our biggest challenges was turning complex digital information into insights that users could actually understand. We also faced challenges while integrating the AI components with the application, handling data consistently, and making sure the results were presented clearly. Iterative testing and debugging helped us improve both the reliability of the system and the user experience.
Accomplishments that we're proud of
We are proud of turning an abstract concept like a digital shadow into a working AI-powered application. We successfully brought together AI, data processing, backend logic, and frontend development into one project. Most importantly, we created something that focuses not only on technology but also on making users more aware of their digital presence.
What we learned
This project taught us that building an AI application is more than integrating a model. We learned how to connect AI with real application workflows, process and interpret data, design a useful user experience, debug integration issues, and continuously improve a product based on testing and feedback. We also learned how important it is to balance technical complexity with simplicity for the end user.
What's next for Shadow-AI
Our next goal is to make Shadow-AI more accurate, personalized, and actionable. We want to expand the types of digital footprints it can analyze, improve the AI-based insights, provide clearer privacy recommendations, and develop stronger visualization and monitoring features so users can continuously understand and manage their digital shadow.
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