Journal of AI, Data Science and Cyber Systems

Integrated AI-Data-Cyber System Design refers to systems where the architecture for artificial intelligence, data frameworks, and cybersecurity systems is designed as a single, cohesive system, rather than as independent, siloed layers. Within this arrangement, data is a key asset, continuously flowing from multiple sources (sensors, applications, networks, users) via data pipelines. These data pipelines are governed to preserve data quality, provenance, and keep data private and available. AI components such as machine learning models, reasoning engines, and autonomous agents, are added to the pipelines for the purposes of analysis and structuring to provide real-time analytics, forecasting, decision-making, and adaptive optimization. Also, cybersecurity is woven into every layer of the architecture, with mechanisms such as secure data ingestion, identity and access management, encryption, zero-trust frameworks, anomalous activity detection, and AI-powered threat intelligence. An integrated AI-Data-Cyber system incorporates feedback loops between the three domains. AI models operationalize and learn from security data. Cybersecurity uses AI to identify and respond to threats. Governance policies adjust in response to risk and system behaviour. The close integration of these components fosters resilience, scalability, and trust. Systems can confidently operate in changing and hostile environments. This architecture is crucial in smart infrastructure, defence, healthcare, and digital enterprises. Intelligent automation, data-driven insight, and cyber defence must coexist. These domains need seamless integration the most.