Real-Time Data Intelligence in Regulated Systems: Designing Secure, Scalable, and Compliant Cloud Architectures on GCP

Abstract

The increased pace of information on the digital ecosystems has increased the strains of real-time information systems in data intelligence particularly in controlled environments such as health, financial systems and governmental systems. This not only implies that these systems need to offer low-latency insights, but also satisfy some of the highest demands in terms of security, privacy, and regulatory compliance. Cloud services, especially Google Cloud Platform (GCP) have become influential enabling factors in the creation of scalable, event-driven architecture to support real-time analytics. Nevertheless, combining the stream processing features with compliance and governance systems is still a major challenge. The given review paper comments on how to develop secure, scale-able, and compliant cloud architectures to provide real-time data intelligence with the help of GCP-native services and the recent ideas behind distributed systems. It talks about, generalizes, and oversimplifies the existing literature on stream processing, distributed storage, zero-trust security, and data governance, pointing out such major problems as latency-compliance trades, tracking data lineage, and enforcing policies in dynamic environments. The article presents a theoretical framework (SCaR-RTI) that balances real-time processing and compliance-by-design with the assistance of architectural and experimental findings and performance analyses. Moreover, the paper outlines new research directions, such as compliance-as-code, privacy-preserving analytics, and confidential computing, AI-driven governance, and data sovereignty-conscious architectures that will define the next generation of regulated cloud systems. This work offers a broad framework to design credible, high-performance real-time data systems by filling the gap between performance engineering and regulatory concerns. The research is expected to inform researchers, cloud architects, and policymakers to come up with systems that are not only efficient and scalable but also in line with the changing regulatory environment.