Enterprise Security Graph Analysis System Development

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Technologies:

C# .NET (6)
Docker (9)
Graph DBs (1)
MSSQL (5)
WinAPI (6)
PostgreSQL (27)
Auto Testing (25)
Linux (23)

Domains:

Business Solutions (12)
Communication and Networking (8)
Finance and Cryptocurrency (7)

Project Goals

The goal of the Enterprise Security Graph Analysis System project was to develop a tool for the security service of a large distributed enterprise to identify and analyze non-obvious connections between employees, departments, and contractors. This system aimed to improve the company's ability to detect potential corporate fraud, embezzlement, and other unusual activities, enabling proactive incident prevention and enhancing overall enterprise security.

Functional Capabilities

  • Graph-Based Data Analysis: The system was built around a graph database (Neo4j), allowing for the representation of relationships between various entities in the enterprise, such as employees, departments, contractors, and transactions. This structure enabled the visualization of complex, non-obvious relationships that are difficult to identify manually.
  • Automated and Manual Data Loading: Information from the enterprise's extensive databases was automatically and manually loaded into the graph system, providing a comprehensive view of relationships within the company.
  • Relationship Visualization: The system allowed users to view relationships between objects, such as employees, contractors, and departments, in the form of graphs. This visualization made it easier for security personnel to identify unusual connections and patterns.
  • In-Depth Analysis of Connections: The graph database excelled at detecting relationships between objects, such as:
    • Family Connections: Identifying relatives working in adjacent areas or in related roles within the company.
    • Capital Ownership: Revealing relationships involving shared ownership of assets or business entities.
    • Interaction with Specific Counterparts: Highlighting interactions with specific contractors or suppliers that may be suspicious.
  • Filtering and Drill-Down: Users could filter data by specific objects, individuals, or departments to focus on areas of concern. This feature enabled a more targeted analysis of potential risks.
  • Rapid Identification of Suspicious Connections: The system provided tools for the rapid identification of key elements and connections that could indicate fraudulent or unusual behavior. This capability was particularly useful in large enterprises where the volume of data makes manual analysis impractical.

Solution Concept

The Enterprise Security Graph Analysis System was designed to address the challenges faced by the security service of a large corporation with over 6000 employees and 60 platforms. Given the scale of the organization, manually detecting non-standard connections between employees, contractors, and departments was a daunting task. The system aimed to automate this process by leveraging the power of graph databases, which are well-suited for representing and analyzing complex relationships.

The backend was developed using C# and .NET, while Neo4j was used as the primary graph database for storing and analyzing relationships. Data was extracted from the enterprise's extensive databases and loaded into the graph system, where it was processed and visualized for the security team.

The use of Neo4j allowed for the detection of non-standard connections, such as relationships between relatives in adjacent areas, ownership of business entities, and unusual interactions with contractors. The data was presented in a user-friendly format, allowing security personnel to quickly identify suspicious connections and take action to prevent fraud and embezzlement.

The development process followed the Agile Scrum methodology, allowing for iterative improvements based on user feedback. The system was designed to be scalable and capable of handling the massive volume of data generated by the enterprise.

Results

  • Reduced Instances of Theft: The implementation of the system led to a significant reduction in theft within the enterprise by quickly identifying suspicious connections and enabling prompt action.
  • Enhanced Incident Response: The ability to detect non-obvious relationships and unusual activities allowed the security team to respond quickly to potential incidents, improving the overall security posture of the enterprise.
  • Reduced Corporate Fraud: By identifying connections that could indicate fraudulent activities, such as relatives working in related departments or unusual contractor relationships, the system helped reduce cases of money leakage and corporate fraud.
  • Proactive Prevention: The system enabled the security team to take a proactive approach to incident prevention by identifying potential risks before they could escalate, reducing the likelihood of thefts, manipulations, and corrupt activities.

Technologies and Architecture

  • Backend Development:
    • C# and .NET: Used for developing the core application and integrating with existing enterprise systems.
  • Graph Database:
    • Neo4j: Implemented as the primary database for storing and analyzing relationships. The graph structure allowed for the visualization of connections that are difficult to detect using traditional relational databases.
  • Database Management:
    • MSSQL and PostgreSQL: Utilized for storing enterprise data and integrating with Neo4j for relationship analysis.
  • Deployment and Integration:
    • Docker: Used for containerizing the application, ensuring consistent deployment across different environments.
  • Operating Systems:
    • Windows and Linux: Supported for both the client-side and server-side components, providing flexibility in deployment options.
  • Development Methodology:
    • Agile Scrum: Adopted to ensure flexibility in development, allowing for iterative improvements and responsiveness to the evolving needs of the security team.

User Cases

  • Security Service Personnel: Security team members used the system to visualize relationships within the enterprise and identify suspicious connections, such as relatives working in related roles or unusual contractor interactions.
  • Management: The system provided management with insights into potential security risks, enabling them to make informed decisions to mitigate threats and improve the overall security posture of the company.
  • Incident Investigators: Investigators used the system to quickly identify connections between employees, departments, and contractors, facilitating efficient investigations into incidents of theft or fraud.

Integration and Development Process

  • Requirements Gathering: The development process began with gathering requirements from the enterprise's security team to understand their specific needs for analyzing relationships and identifying potential security risks.
  • System Design and Architecture: The system architecture was designed to support the analysis of complex relationships using Neo4j. The backend was developed using C# and .NET, while Docker was used for containerization to ensure consistent deployment.
  • Team Formation and Leadership: A team of software developers, system architects, and data analysts was formed to develop and implement the system. The development process followed the Agile Scrum methodology, allowing for continuous feedback and iterative improvements.
  • Implementation and Testing: The system was implemented iteratively, with regular testing to ensure that the graph analysis capabilities met the needs of the security team. The use of Neo4j allowed for efficient visualization and analysis of relationships, providing valuable insights into potential risks.

Client Benefits

  • Improved Security: The system significantly improved the company's security posture by providing tools for quickly identifying non-obvious relationships and potential risks, enabling the security team to take proactive measures.
  • Reduced Fraud and Theft: By identifying connections that could indicate fraudulent activities, the system helped reduce instances of theft and corporate fraud, leading to significant cost savings for the company.
  • Enhanced Efficiency: The automation of relationship analysis reduced the time and effort required for the security team to identify suspicious connections, allowing them to focus on addressing risks rather than manually searching for them.
  • Proactive Risk Management: The system enabled the security team to proactively prevent incidents by identifying potential risks before they could escalate, reducing the likelihood of thefts, manipulations, and corrupt activities.