As organizations shift to AI-native operations, security becomes a non-negotiable pillar of deployment. Nowhere is this more evident than in the insurance industry, where AI is automating high-stakes workflows—underwriting, claims processing, and agent support—at massive scale. But with this transformation comes new and evolving risks.

In this session, we’ll explore a security-first approach to implementing AI in regulated, data-sensitive environments. Drawing from real-world deployments in global insurance firms, we’ll walk through a modular AI architecture that combines machine learning, NLP, robotic process automation (RPA), and predictive analytics—augmented with layered security controls, auditability, and real-time monitoring.

You’ll learn how insurers have reduced fraud by over 60%, automated up to 70% of routine operations, and shortened decision cycles by more than 50%—all while embedding traceability, model risk management, and compliance with frameworks like SOC 2, HIPAA, and GDPR. Topics will include secure prompt engineering, output validation, adversarial input detection, and the implementation of observability stacks to detect model drift and access anomalies in real time.

This talk is designed for security engineers, DevSecOps leaders, and AI architects seeking to safeguard the shift to AI-native enterprise environments. We’ll offer practical guidance on integrating zero-trust principles into AI systems, establishing model governance workflows, and designing resilient pipelines that protect both the business and the customer.

Attendees will leave with a proven blueprint for deploying secure, explainable, and operationally reliable AI systems at scale—capable of transforming enterprise performance without compromising trust or compliance.