Cyber-Aware AI and Serverless Analytics for Cloud-Native SAP and Apache Iceberg Architectures

Abstract

The transition toward cloud-native enterprise architectures has accelerated the adoption of SAP systems and modern open table formats such as Apache Iceberg to support scalable analytics and data-driven decision-making. However, this evolution also introduces significant cybersecurity, governance, and operational challenges, particularly in serverless computing environments. This paper presents a cyber-aware AI and serverless analytics framework for cloud-native SAP and Apache Iceberg architectures. The proposed approach integrates machine learning models with serverless analytics pipelines to monitor data access patterns, transactional behavior, and network interactions across SAP workloads. Cyber risk intelligence is embedded into the analytics layer to enable proactive detection of anomalies, security threats, and policy violations. Apache Iceberg is leveraged to ensure reliable data versioning, schema evolution, and time-travel analytics, supporting trustworthy AI model training and auditability. The framework is designed to be cloud-native, elastic, and cost-efficient while maintaining strong security and compliance guarantees. Experimental evaluation demonstrates improved threat detection accuracy, optimized resource utilization, and enhanced resilience compared to traditional monolithic analytics architectures. The results highlight the effectiveness of combining AI, serverless computing, and cyber-aware analytics for secure and scalable enterprise data platforms.