As organizations accelerate their shift toward AI-native systems, the intersection of machine learning, cloud infrastructure, and security has never been more critical. Nowhere is this convergence more visible than in the cyber insurance sector, where AI-powered risk scorecards are being used to assess exposure, determine policy terms, and recommend remediation in real time. This talk explores how these platforms are engineered for trust—through secure design, transparent modeling, and continuous risk validation.

Built on modern DevSecOps principles, these systems ingest real-time security telemetry, behavioral signals, and threat intelligence to produce dynamic, multidimensional risk profiles for Small and Medium Enterprises (SMEs). But beyond the model outputs lies a complex architecture that must maintain integrity, auditability, and regulatory compliance across the ML lifecycle. We'll examine how teams are securing training pipelines, managing data lineage, automating compliance mappings, and implementing controls like RBAC, CI/CD attestation, explainable AI, and runtime monitoring to meet insurer-grade reliability and governance standards.

Real-world case studies will highlight how secure MLOps and infrastructure-as-code practices are applied to deploy AI-driven systems in production environments—balancing model agility with operational resilience. Attendees will also learn how AI-native risk engines integrate into legacy underwriting workflows and reshape how insurers engage with clients—not just after a breach, but in active risk mitigation and security posture improvement.

If you’re a security engineer, ML practitioner, or DevSecOps leader navigating the challenges of securing AI systems in regulated domains, this session offers a blueprint for delivering not just performant models, but systems of trust. Learn how the cyber insurance industry’s transformation is setting the tone for securing the next generation of AI-native platforms—where accountability, explainability, and resilience are non-negotiable.