Scalable Data Query Microservice and Visualization Platform for Cybersecurity Risk Monitoring

The client faces difficulties in monitoring numerous API connections, data flows, and traffic patterns across a complex infrastructure, risking vulnerabilities due to the proliferation of API integrations and microservices. Ensuring real-time detection, risk prioritization, and comprehensive visualization of vast data volumes is critical for proactive security oversight.

A mid-sized cybersecurity firm specializing in data analysis and threat detection, focusing on infrastructure security and anomaly detection.

• Develop a scalable, fault-tolerant data query microservice capable of aggregating and processing over 6.8 million events per minute, with a peak capacity of 300,000 events per second.
• Implement real-time risk detection algorithms that classify risks into low, medium, and critical categories, providing a scoring system from 1 to 100.
• Enable dynamic risk severity tracking over multiple timeframes (e.g., 30 minutes, 2 hours, 24 hours) to facilitate trend analysis.
• Design an integrated internal dashboard allowing users to visualize multiple metrics simultaneously without overlap, with filtering capabilities across at least 86 parameters.
• Support rapid feature deployment within a 2-3 week sprint cycle to gather early feedback and refine functionalities.

• Massively scalable data ingestion pipeline supporting over 6.8 million events per minute, ensuring high availability and fault tolerance.
• Event-driven architecture leveraging Apache Kafka and Apache Druid for efficient data processing and storage.
• Algorithmic risk detection and classification with severity scoring (1–100) and real-time updates.
• Visualization dashboard displaying multiple metrics simultaneously with filtering, sorting, and time-based trend analysis.
• Customizable filters (at least 86) to tailor data views by various parameters for targeted risk analysis.

• Existing cybersecurity and data monitoring systems to feed data streams
• External data sources for enriching analysis context
• User authentication and role management systems

• Ability to process up to 6.8 million events per minute with minimal latency
• System uptime and fault tolerance to support continuous monitoring
• Security standards to protect data integrity and access control
• Scalability to support future growth in data volume and analytical complexity

This system is expected to significantly enhance cybersecurity risk monitoring capabilities by enabling real-time analysis of massive data streams, resulting in more accurate and timely risk prioritization. The implementation aims to achieve high performance with over 6.8 million events processed per minute, streamline operations, shorten response times, and support rapid feature deployment—ultimately strengthening the client’s position in cybersecurity threat mitigation and enabling successful funding rounds.