In today’s rapidly evolving threat landscape, Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Yet, traditional insurance underwriting models—based on static questionnaires and infrequent assessments—are no longer effective. This session explores how data professionals are playing a vital role in the development of AI-powered cyber risk platforms that provide dynamic, real-time insights into organizational exposure and risk posture.

At the heart of these platforms are rich data pipelines that ingest and analyze diverse information sources—including network vulnerabilities, breach history, endpoint telemetry, and behavioral analytics. We’ll examine how modern data architecture—including SQL-based warehousing, cloud-native ETL workflows, and predictive analytics—is used to generate actuarial-grade risk scores that inform insurance pricing, coverage decisions, and proactive remediation.

The talk will focus on the full lifecycle of data in a cyber insurance context: from ingestion and data transformation, through modeling and governance, to visualizing insights with reporting tools like Power BI. Attendees will learn how to ensure data quality, implement schema design patterns that support scalability, and apply machine learning outputs responsibly within regulated environments. The session also covers how data teams manage compliance mapping (e.g., NIST, CIS), track lineage, and deliver transparency into AI decision-making pipelines.

Case studies will highlight measurable outcomes including reduced policy issuance time, improved underwriting accuracy, and enhanced client engagement. Whether you’re working in data engineering, BI, or analytics, you’ll leave with a practical framework for how your skills can drive meaningful innovation in a growing sector—where data is not just an asset, but the foundation for securing the digital economy.