As SMEs face rising cyber threats, the insurance industry is responding with AI-powered risk platforms that shift underwriting from static surveys to real-time, data-driven decisions. This session explores how these intelligent systems are built on the Azure ecosystem—leveraging cloud-native services, machine learning, and secure data pipelines to enable next-generation cyber insurance for small and medium enterprises.

Attendees will gain a deep understanding of how Microsoft Azure services—such as Azure Machine Learning, Synapse Analytics, Azure Functions, and Defender for Cloud—are orchestrated to collect and process diverse data sources including live network telemetry, threat intelligence feeds, and behavioral analytics. These signals power AI models that generate dynamic, multidimensional risk profiles, helping insurers evaluate cyber exposure, recommend proactive remediation, and align policy pricing with real-world threat conditions.

We’ll walk through the architecture of a typical Azure-based risk platform, from data ingestion and feature engineering to model scoring and API-based integration with underwriting systems. You'll learn how platform builders are using Azure DevOps, Key Vault, Logic Apps, and compliance tooling like Microsoft Purview to ensure operational integrity, regulatory alignment, and explainable AI outputs.

Real-world case studies will illustrate how early adopters have achieved faster policy issuance, improved loss ratios, and enhanced customer engagement through these cloud-native solutions. This session will also highlight key DevOps and MLOps practices that support continuous model improvement and system scalability.

Whether you're building secure data applications, deploying machine learning in production, or enabling real-time intelligence on Azure, this talk provides a practical roadmap for turning cybersecurity data into business value—through a secure, scalable, and AI-powered cloud architecture.