Journal of AI, Data Science and Cyber Systems

Scalable Big Data Systems for Intelligent Cyber Platforms relate to the engineering and design systems that allow the capture, storing, processing, and analyzing of large, rapidly changing, and varied streams of cybersecurity data to assist intelligent automated defense systems. These systems process large data streams in real time and include: network traffic, logs, telemetry, alerts, and threat intelligence. Systems must be designed to have low latency, high availability, and fault tolerant systems. Both horizontal and vertical scalability is essential so that cyber platforms evolve with growing network and cloud architectures as well as complex attack surfaces. Such systems usually utilize distributed computing, data lakes, cloud-native architecture, stream processing frameworks, and parallel analytics engines to assist with real-time and batch intelligence workloads. Machine learning and AI are integrated into the data pipeline for continuous threat detection, behavior analytics, automated response, risk scoring, and other automated response functions. Strong data governance, security controls, and lifecycle management equally aid in ensuring integrity, confidentiality, and compliance at scale. Big, scalable data systems are the core of intelligent cyber platforms, transforming high-velocity, raw data into actionable insights, and enabling cybersecurity functions to be faster, more accurate, and more resilient in expansive, intricate digital landscapes.