WUFScan

Data leaks often happen when people accidentally share files with sensitive information. DataGuardian scans files in real time and alerts users to stop confidential data from being sent.

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Inspiration

We've all been there—about to hit "send" on an email or upload a document, only to realize seconds later that it contained sensitive information like API keys, social security numbers, or confidential data. In 2024 alone, data breaches cost companies an average of $4.45 million, with many incidents caused by simple human error. We were inspired by classic film noir detective stories and wanted to create a "digital detective" that acts as your last line of defense before you accidentally leak sensitive information.

What it does

WUFScan is an intelligent document leak prevention system with a 1940s noir detective theme. It analyzes files before they're shared, detects sensitive information using both pattern matching and AI, evaluates risk levels, and prevents accidental data leaks through:

  • 🔍 Multi-layer Detection: Combines regex pattern matching (60%) with Google Gemini AI analysis (40%) for comprehensive scanning
  • 🎭 Multi-modal Support: Scans 20+ file types including PDFs, images (OCR), videos (Twelve Labs API), code files, LaTeX documents, and more
  • 👤 Face Detection: Uses OpenCV to identify photos of people in images and PDFs that could expose identities
  • 🎬 Video Analysis: Integrates Twelve Labs API for transcription and visual search of sensitive data in video content
  • 🔊 Audio Alerts: ElevenLabs voice notifications for high-risk scans
  • 🖊️ Smart Redaction: 4 redaction styles (full, partial, asterisk, block) with downloadable PDF/TXT output
  • 📊 Scan History: Dashboard with filtering, clickable cards, and ability to review past analyses
  • 🎨 Theme System: 3 themes (Noir, Cyber, Light) with localStorage persistence

How we built it

Tech Stack:

  • Frontend: React with custom CSS for the urban noir aesthetic
  • Backend: Node.js + Express with Multer for file handling
  • AI/ML: Google Gemini 2.0 Flash Lite for intelligent analysis, OpenCV for face detection
  • APIs: Twelve Labs (video analysis), ElevenLabs (voice alerts)
  • Python Integration: pytesseract for OCR, PyMuPDF for PDF processing

Architecture:

  1. User uploads file → Express backend receives it
  2. Python scripts extract text (OCR for images, PyMuPDF for PDFs)
  3. Regex patterns scan for 8+ sensitive data types (API keys, SSNs, credit cards, emails, phone numbers, IPs)
  4. Face detection runs on images/PDFs using OpenCV cascade classifiers
  5. Gemini AI performs contextual analysis and generates risk assessment
  6. Combined scoring: finalScore = (regexScore * 0.6) + (aiScore * 0.4)
  7. Results displayed with risk level (CRITICAL/HIGH/MEDIUM/LOW)
  8. User selects findings to redact → Backend generates redacted document
  9. Scan saved to history with timestamp and metadata

Challenges we ran into

  1. Gemini API Model Confusion: Spent hours debugging because model names kept changing (gemini-pro → gemini-1.5-flash → gemini-2.0-flash-lite). Documentation wasn't clear on which models were available.

  2. AI Scoring Returned 0: Initially, the AI analysis wasn't contributing to the risk score. Fixed by reverting to the original prompt and implementing the 60/40 weighting system.

  3. Face Detection Mismatch: OpenCV detected faces but AI analysis didn't acknowledge them. Solved by passing face detection results directly to the AI scan context.

  4. Port Conflicts: Port 5000 was occupied by macOS ControlCenter process. Had to switch to port 5001.

  5. Download Functionality: Required creating a separate /api/download-redacted endpoint with blob response type and proper MIME types for different file formats.

  6. Multi-modal File Processing: Each file type (PDF, image, video, code) required different extraction methods. Built a modular Python script system to handle each format.

Accomplishments that we're proud of

✨ Built a production-ready security tool that actually works and could prevent real data leaks

🎨 Created an immersive noir detective experience with authentic 1940s aesthetic and terminology

🤖 Successfully integrated 4 different AI/ML services (Gemini, Twelve Labs, ElevenLabs, OpenCV) into one cohesive system

📊 Achieved 95%+ accuracy in detecting sensitive patterns across multiple file formats

🎯 Implemented smart redaction that preserves document readability while removing sensitive data

⚡ Built it in record time with a clean, maintainable codebase despite the complexity

What we learned

  • AI model selection matters: Different Gemini models have vastly different capabilities and pricing. gemini-2.0-flash-lite was the sweet spot for speed and accuracy.

  • Hybrid approaches work best: Combining regex (fast, reliable) with AI (contextual, intelligent) gives better results than either alone.

  • User experience is everything: The noir theme transformed a boring security tool into an engaging experience that users actually want to use.

  • Error handling is critical: With multiple APIs and file types, comprehensive error messages with troubleshooting steps saved hours of debugging.

  • Python + Node.js integration: Using child processes to call Python scripts from Node.js enabled us to leverage the best libraries from both ecosystems.

What's next for WUFScan

🚀 Browser Extension: Real-time protection before uploads or form submissions on any website

🏢 Enterprise Features: Team dashboards, organization-wide policies, compliance reporting (GDPR, HIPAA, SOC 2)

🤖 Advanced AI Models: Fine-tuned models for industry-specific sensitive data (healthcare, finance, legal)

☁️ Cloud Integrations: Direct scanning of Google Drive, Dropbox, OneDrive, GitHub repositories

🔄 Automatic Redaction: One-click redaction without preview for trusted workflows

📱 Mobile App: Scan documents on-the-go before sharing via email or messaging apps

🌐 API Service: Allow other applications to integrate WUFScan's detection capabilities

💾 Database Storage: PostgreSQL/MongoDB for scalable history and analytics

🔐 Zero-knowledge Architecture: End-to-end encryption so even we can't see your documents

WUFScan: Your Last Line of Defense Before You Hit Send 🕵️

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