truscanner 0.2.6

Open-Source Static Analysis for Privacy Data Flows

truScanner from truConsent

Open-Source Static Analysis for Privacy Data Flows

truScanner is a static code analysis tool designed to discover and analyze personal data elements in your source code. It helps developers and security teams identify privacy-related data flows and generate comprehensive reports.

📦 PyPI Project • 🌐 App Dashboard

🚀 Features

• Comprehensive Detection: Identifies 300+ personal data elements (PII, financial data, device identifiers, etc.)
• Interactive Menu: Arrow-key navigable menu for selecting output formats
• Real-time Progress: Visual progress indicator during scanning
• Multiple Report Formats: Generate reports in TXT, Markdown, or JSON format
• AI-Powered Enhancement: Optional integration with Ollama or OpenAI for deeper context
• Backend Integration: Optional upload to backend API for centralized storage
• Auto-incrementing Reports: Automatically manages report file naming to prevent overwrites

truScanner CLI

📦 Installation

Prerequisites

• Python 3.9 or higher
• ollama (optional, for local AI scanning)

Quick Install

Using pip:

pip install truscanner

Using uv:

uv pip install truscanner

Verify installation:

truscanner --help

🛠️ Usage

Basic Usage

Scan a directory with the interactive menu:

truscanner scan <directory_path>

Example

truscanner scan ./src
truscanner scan ./my-project
truscanner scan C:\Users\username\projects\my-app

Python API Usage

Use truScanner directly from Python:

import truscanner

# Local path
check = truscanner("/path/to/project")

# file:// URL also works
check = truscanner("file:///Users/username/project")

# Optional explicit call style
check = truscanner.scan("/path/to/project", with_ai=False)

Quick smoke check script:

uv run python scripts/check_truscanner_api.py ./src

Interactive Workflow

1. Select Output Format:

   • Use arrow keys (↑↓) to navigate
   • Press Enter to select
   • Options: txt, md, json, or All (generates all three formats)

2. Scanning Progress:

   • Real-time progress bar shows file count and percentage
   • Example: Scanning: 50/200 (25%) [████████░░░░░░░░░░░░] filename.js

3. AI Enhanced Scan (Optional):

   • After the initial scan, you'll be prompted: Do you want to use Ollama/AI for enhanced PII detection (find what regex missed)? (Y, N):
   • This uses local LLMs (via Ollama) or OpenAI to find complex PII.
   • Live scanning timer: AI Scanning: filename.js... (5.2s taken)

4. Report Generation:

   • Reports are saved in reports/{directory_name}/ folder
   • Files are named: truscan_report.txt, truscan_report.md, truscan_report.json
   • Subsequent scans auto-increment: truscan_report1.txt, truscan_report2.txt, etc.
   • AI findings are saved with _llm suffix.

5. Backend Upload (Optional):

   • After reports are saved, you'll be prompted: Do you want to upload the scan report for the above purpose? (Y, N):
   • Enter Y to upload scan results to backend API
   • View your uploaded scans and analytics at app.truconsent.io

Command Options

truscanner scan <directory> [OPTIONS]

Options:
  --with-ai          Enable AI/LLM scanner directly
  --ai-mode          AI scan mode: fast, balanced, or full (default: balanced)
  --personal-only    Only report personal identifiable information (PII)
  --help             Show help message

AI Speed vs Coverage Modes

Use --ai-mode to control AI scan behavior:

• fast: Small prompts, fastest runtime, may skip very large low-signal files
• balanced (default): Good speed while keeping broad file coverage
• full: Largest context and highest coverage, slowest runtime

Examples:

truscanner scan ./src --ai-mode fast
truscanner scan ./src --ai-mode balanced
truscanner scan ./src --ai-mode full

📊 Report Output

Report Location

Reports are saved in: reports/{sanitized_directory_name}/

Report Formats

• TXT Report (truscan_report.txt): Plain text format, easy to read
• Markdown Report (truscan_report.md): Formatted markdown with headers and code blocks
• JSON Report (truscan_report.json): Structured JSON data for programmatic access

Report Contents

Each report includes:

• Scan Report ID: Unique 32-bit hash identifier
• Summary: Total findings, time taken, files scanned
• Findings by File: Detailed list of data elements found in each file
• Summary by Category: Aggregated statistics by data category

Report ID

Each scan generates a unique Scan Report ID (32-bit MD5 hash) that:

• Appears in the terminal after scanning
• Is included at the top of all generated report files
• Can be used to track and reference specific scans

🔧 Configuration

The truscanner package is pre-configured with the live backend URL for seamless scan uploads. No additional configuration is required.

📁 Project Structure

truscanner/
├── src/
│   ├── main.py              # CLI entry point
│   ├── regex_scanner.py     # Core scanning engine
│   ├── ai_scanner.py        # AI/LLM scanning engine
│   ├── report_utils.py      # Report utilities
│   └── utils.py             # Utilities
├── data_elements/           # Data element definitions
├── reports/                 # Generated reports
├── pyproject.toml           # Project configuration
└── README.md

📝 Change Policy

For this repository, every code or behavior change must include a matching README update in the same change.

This includes:

• CLI flags, prompts, defaults, scan behavior, output format changes
• Python API changes (import truscanner, return schema, parameters)
• Dependency/runtime requirements
• Report format/location updates

🤝 Support

For issues, questions, or contributions, please contact: [e-mail]

MIT License - see LICENSE file for details