https://github.com/ALW1EZ/PANO
🔍 PANO: Advanced OSINT investigation platform combining graph visualization, timeline analysis, and AI assistance to uncover hidden connections in data. Built with Python and modern Qt.
https://github.com/ALW1EZ/PANO
cybersecurity investigation investigation-platform osint
Last synced: 10 months ago
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🔍 PANO: Advanced OSINT investigation platform combining graph visualization, timeline analysis, and AI assistance to uncover hidden connections in data. Built with Python and modern Qt.
- Host: GitHub
- URL: https://github.com/ALW1EZ/PANO
- Owner: ALW1EZ
- License: other
- Created: 2024-12-30T21:50:44.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-26T01:31:34.000Z (11 months ago)
- Last Synced: 2025-04-26T02:36:13.795Z (11 months ago)
- Topics: cybersecurity, investigation, investigation-platform, osint
- Language: Python
- Homepage:
- Size: 716 KB
- Stars: 134
- Watchers: 1
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-starts - ALW1EZ/PANO - 🔍 PANO: Advanced OSINT investigation platform combining graph visualization, timeline analysis, and AI assistance to uncover hidden connections in data. Built with Python and modern Qt. (osint)
- awesome-rainmana - ALW1EZ/PANO - 🔍 PANO: Advanced OSINT investigation platform combining graph visualization, timeline analysis, and AI assistance to uncover hidden connections in data. Built with Python and modern Qt. (Python)
README
# PANO - Platform for Analysis and Network Operations
[](https://creativecommons.org/licenses/by-nc/4.0/)
[](https://www.python.org/downloads/)
[](https://wiki.qt.io/Qt_for_Python)
[](https://github.com/ALW1EZ/PANO/)
**PANO** is a powerful OSINT investigation platform that combines graph visualization, timeline analysis, and AI-powered tools to help you uncover hidden connections and patterns in your data.
[Getting Started](#getting-started) • [Features](#features) • [Documentation](#development-guide) • [Contributing](#contributing)
---
https://github.com/user-attachments/assets/6b95e97a-ffdd-4056-b12b-f268d8e5d565
https://github.com/user-attachments/assets/af0c0f65-7b79-417c-a678-a789d42a6003
https://github.com/user-attachments/assets/568b4c28-be78-4559-a607-899fca2f9613
https://github.com/user-attachments/assets/6aa191eb-09de-41d5-b96f-4a2e32d6a0c2
https://github.com/user-attachments/assets/96377b80-e137-4eb4-9387-a0db4e49c2c1
https://github.com/user-attachments/assets/4228bda0-6c13-4ea9-bc07-d87abe367e10
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/ALW1EZ/PANO.git
cd PANO
```
2. Run the application:
- Linux: `./start_pano.sh`
- Windows: `start_pano.bat`
The startup script will automatically:
- Check for updates
- Set up the Python environment
- Install dependencies
- Launch PANO
In order to use Email Lookup transform
You need to login with GHunt first.
After starting the pano via starter scripts;
1. Select venv manually
- Linux: `source venv/bin/activate`
- Windows: `call venv\Scripts\activate`
3. See how to login [here](https://github.com/mxrch/GHunt/?tab=readme-ov-file#login)
## 💡 Quick Start Guide
1. **Create Investigation**: Start a new investigation or load an existing one
2. **Add Entities**: Drag entities from the sidebar onto the graph
3. **Discover Connections**: Use transforms to automatically find relationships
4. **Analyze**: Use timeline and map views to understand patterns
5. **Save**: Export your investigation for later use
## 🔍 Features
### 🕸️ Core Functionality
- **Interactive Graph Visualization**
- Drag-and-drop entity creation
- Multiple layout algorithms (Circular, Hierarchical, Radial, Force-Directed)
- Dynamic relationship mapping
- Visual node and edge styling
- **Timeline Analysis**
- Chronological event visualization
- Interactive timeline navigation
- Event filtering and grouping
- Temporal relationship analysis
- **Map Integration**
- Geographic data visualization
- Location-based analysis
- Interactive mapping features
- Coordinate plotting and tracking
### 🎯 Entity Management
- **Supported Entity Types**
- 📧 Email addresses
- 👤 Usernames
- 🌐 Websites
- 🖼️ Images
- 📍 Locations
- ⏰ Events
- 📝 Text content
- 🔧 Custom entity types
### 🔄 Transform System
- **Email Analysis**
- Google account investigation
- Calendar event extraction
- Location history analysis
- Connected services discovery
- **Username Analysis**
- Cross-platform username search
- Social media profile discovery
- Platform correlation
- Web presence analysis
- **Image Analysis**
- Reverse image search
- Visual content analysis
- Metadata extraction
- Related image discovery
### 🤖 AI Integration
- **PANAI**
- Natural language investigation assistant
- Automated entity extraction and relationship mapping
- Pattern recognition and anomaly detection
- Multi-language support
- Context-aware suggestions
- Timeline and graph analysis
## 🧩 Core Components
### 📦 Entities
Entities are the fundamental building blocks of PANO. They represent distinct pieces of information that can be connected and analyzed:
- **Built-in Types**
- 📧 Email: Email addresses with service detection
- 👤 Username: Social media and platform usernames
- 🌐 Website: Web pages with metadata
- 🖼️ Image: Images with EXIF and analysis
- 📍 Location: Geographic coordinates and addresses
- ⏰ Event: Time-based occurrences
- 📝 Text: Generic text content
- **Properties System**
- Type-safe property validation
- Automatic property getters
- Dynamic property updates
- Custom property types
- Metadata support
### ⚡ Transforms
Transforms are automated operations that process entities to discover new information and relationships:
- **Operation Types**
- 🔍 Discovery: Find new entities from existing ones
- 🔗 Correlation: Connect related entities
- 📊 Analysis: Extract insights from entity data
- 🌐 OSINT: Gather open-source intelligence
- 🔄 Enrichment: Add data to existing entities
- **Features**
- Async operation support
- Progress tracking
- Error handling
- Rate limiting
- Result validation
### 🛠️ Helpers
Helpers are specialized tools with dedicated UIs for specific investigation tasks:
- **Available Helpers**
- 🔍 Cross-Examination: Analyze statements and testimonies
- 👤 Portrait Creator: Generate facial composites
- 📸 Media Analyzer: Advanced image processing and analysis
- 🔍 Base Searcher: Search near places of interest
- 🔄 Translator: Translate text between languages
- **Helper Features**
- Custom Qt interfaces
- Real-time updates
- Graph integration
- Data visualization
- Export capabilities
## 👥 Contributing
We welcome contributions! To contribute to PANO:
1. Fork the repository at https://github.com/ALW1EZ/PANO/
2. Make your changes in your fork
3. Test your changes thoroughly
4. Create a Pull Request to our main branch
5. In your PR description, include:
- What the changes do
- Why you made these changes
- Any testing you've done
- Screenshots if applicable
> **Note**: We use a single `main` branch for development. All pull requests should be made directly to `main`.
## 📖 Development Guide
Click to expand development documentation
### System Requirements
- Operating System: Windows or Linux
- Python 3.11+
- PySide6 for GUI
- Internet connection for online features
### Custom Entities
Entities are the core data structures in PANO. Each entity represents a piece of information with specific properties and behaviors. To create a custom entity:
1. Create a new file in the `entities` folder (e.g., `entities/phone_number.py`)
2. Implement your entity class:
```python
from dataclasses import dataclass
from typing import ClassVar, Dict, Any
from .base import Entity
@dataclass
class PhoneNumber(Entity):
name: ClassVar[str] = "Phone Number"
description: ClassVar[str] = "A phone number entity with country code and validation"
def init_properties(self):
"""Initialize phone number properties"""
self.setup_properties({
"number": str,
"country_code": str,
"carrier": str,
"type": str, # mobile, landline, etc.
"verified": bool
})
def update_label(self):
"""Update the display label"""
self.label = self.format_label(["country_code", "number"])
```
### Custom Transforms
Transforms are operations that process entities and generate new insights or relationships. To create a custom transform:
1. Create a new file in the `transforms` folder (e.g., `transforms/phone_lookup.py`)
2. Implement your transform class:
```python
from dataclasses import dataclass
from typing import ClassVar, List
from .base import Transform
from entities.base import Entity
from entities.phone_number import PhoneNumber
from entities.location import Location
from ui.managers.status_manager import StatusManager
@dataclass
class PhoneLookup(Transform):
name: ClassVar[str] = "Phone Number Lookup"
description: ClassVar[str] = "Lookup phone number details and location"
input_types: ClassVar[List[str]] = ["PhoneNumber"]
output_types: ClassVar[List[str]] = ["Location"]
async def run(self, entity: PhoneNumber, graph) -> List[Entity]:
if not isinstance(entity, PhoneNumber):
return []
status = StatusManager.get()
operation_id = status.start_loading("Phone Lookup")
try:
# Your phone number lookup logic here
# Example: query an API for phone number details
location = Location(properties={
"country": "Example Country",
"region": "Example Region",
"carrier": "Example Carrier",
"source": "PhoneLookup transform"
})
return [location]
except Exception as e:
status.set_text(f"Error during phone lookup: {str(e)}")
return []
finally:
status.stop_loading(operation_id)
```
### Custom Helpers
Helpers are specialized tools that provide additional investigation capabilities through a dedicated UI interface. To create a custom helper:
1. Create a new file in the `helpers` folder (e.g., `helpers/data_analyzer.py`)
2. Implement your helper class:
```python
from PySide6.QtWidgets import (
QWidget, QVBoxLayout, QHBoxLayout, QPushButton,
QTextEdit, QLabel, QComboBox
)
from .base import BaseHelper
from qasync import asyncSlot
class DummyHelper(BaseHelper):
"""A dummy helper for testing"""
name = "Dummy Helper"
description = "A dummy helper for testing"
def setup_ui(self):
"""Initialize the helper's user interface"""
# Create input text area
self.input_label = QLabel("Input:")
self.input_text = QTextEdit()
self.input_text.setPlaceholderText("Enter text to process...")
self.input_text.setMinimumHeight(100)
# Create operation selector
operation_layout = QHBoxLayout()
self.operation_label = QLabel("Operation:")
self.operation_combo = QComboBox()
self.operation_combo.addItems(["Uppercase", "Lowercase", "Title Case"])
operation_layout.addWidget(self.operation_label)
operation_layout.addWidget(self.operation_combo)
# Create process button
self.process_btn = QPushButton("Process")
self.process_btn.clicked.connect(self.process_text)
# Create output text area
self.output_label = QLabel("Output:")
self.output_text = QTextEdit()
self.output_text.setReadOnly(True)
self.output_text.setMinimumHeight(100)
# Add widgets to main layout
self.main_layout.addWidget(self.input_label)
self.main_layout.addWidget(self.input_text)
self.main_layout.addLayout(operation_layout)
self.main_layout.addWidget(self.process_btn)
self.main_layout.addWidget(self.output_label)
self.main_layout.addWidget(self.output_text)
# Set dialog size
self.resize(400, 500)
@asyncSlot()
async def process_text(self):
"""Process the input text based on selected operation"""
text = self.input_text.toPlainText()
operation = self.operation_combo.currentText()
if operation == "Uppercase":
result = text.upper()
elif operation == "Lowercase":
result = text.lower()
else: # Title Case
result = text.title()
self.output_text.setPlainText(result)
```
## 📄 License
This project is licensed under the Creative Commons Attribution-NonCommercial (CC BY-NC) License.
**You are free to:**
- ✅ Share: Copy and redistribute the material
- ✅ Adapt: Remix, transform, and build upon the material
**Under these terms:**
- ℹ️ Attribution: You must give appropriate credit
- 🚫 NonCommercial: No commercial use
- 🔓 No additional restrictions
## 🙏 Acknowledgments
Special thanks to all library authors and contributors who made this project possible.
## 👨💻 Author
Created by ALW1EZ with AI ❤️