The Data Leak Detection & Prevention System is a security-focused application designed to analyze and monitor sensitive data to detect potential leaks, anomalies, and high-risk individuals. This prototype project utilizes advanced search algorithms, pattern recognition, and anomaly detection to process structured data inputs, such as passenger information and travel records, and provide real-time alerts and actionable insights.
This project serves as a foundation for a scalable solution that can be extended to incorporate more complex features like Big Data processing, AI integration, and comprehensive security measures.
- Data Leak Detection: Identifies and prevents unauthorized data access or potential leaks.
- High-Risk Individual Identification: Cross-references data against predefined risk profiles to flag potential threats.
- Anomaly Detection: Detects abnormal patterns and behaviors within data sets to identify illicit activities.
- Real-Time Alerts: Provides actionable insights through instant notifications for suspicious activities.
- Advanced Search Capabilities: Utilizes optimized search algorithms for fast and accurate data retrieval.
- Programming Language: Python
- Core Library: Custom search and pattern recognition algorithms
- Security: Secure data handling and monitoring mechanisms
- Data Sources: Structured data inputs (e.g., passenger information, travel records)
- Deployment: Local server setup for prototype testing
- Data Leak Detection & Prevention: Enhance security by monitoring and analyzing sensitive data.
- Risk Assessment: Identify high-risk individuals and make informed decisions on entry or denial.
- Anomaly Detection: Spot irregular activities or patterns in data, aiding in fraud prevention and security enforcement.
- Real-Time Alerts: Provide real-time feedback and insights on potential threats or suspicious activities.
- Python 3.x
- Virtual environment setup (optional but recommended)
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Clone the repository:
git clone https://github.com/your-username/data-leak-detection-system.git