The cyber insurance industry is undergoing a major shift—from static, questionnaire-based underwriting to dynamic, data-driven platforms that assess real-time risk. At the core of this transformation are AI-powered cyber risk scorecards—intelligent systems that analyze vast volumes of security and behavioral data to help insurers accurately quantify and price risk for Small and Medium Enterprises (SMEs).

In this session, we’ll explore how modern cyber risk platforms are built using the Microsoft data ecosystem, including SQL Server, Azure Synapse Analytics, Power BI, and ML services. Attendees will gain insights into how structured and unstructured security data—such as network vulnerabilities, breach histories, and user behavior—is ingested, cleaned, and modeled within SQL-based pipelines to power predictive risk scoring engines.

We’ll discuss the database design and data warehousing strategies that support scalable analytics and real-time decision-making, including the use of partitioning, columnstore indexes, and automated ETL workflows. You'll also learn how actuarial outputs and compliance mapping are supported through T-SQL reporting, Power BI dashboards, and data governance practices—all critical in the highly regulated insurance domain.

Real-world case studies will demonstrate how insurers are using these platforms to reduce policy issuance time, improve loss ratios, and enable proactive engagement with customers on cyber hygiene. This talk will highlight how SQL Server professionals can play a key role in building secure, AI-ready data architectures that support risk visibility at scale.

If you're working with SQL Server and interested in real-time analytics, cloud integration, or applying AI to business-critical applications, this session will provide practical examples and architectural patterns for transforming raw data into trusted cyber risk intelligence that drives meaningful impact.