As AI becomes deeply integrated into enterprise data ecosystems—fueling innovation in finance, healthcare, manufacturing, and national infrastructure—its security cannot be left to chance. The growing threat of adversarial attacks, model inversion, and data poisoning exposes vulnerabilities not only in code, but in the very datasets that power intelligent systems.

This session introduces the Secure-by-Design (SbD) approach to AI security—a proactive, lifecycle-driven framework that integrates security from data collection through to deployment and monitoring. Tailored for data scientists, AI engineers, security professionals, and enterprise architects, the session demonstrates how to operationalize SbD using secure data pipelines, threat modeling for ML systems, adversarial robustness testing, and API governance.

Drawing on real-world implementations and emerging global standards—including the NIST AI Risk Management Framework and ISO/IEC 42001—we'll explore sector-specific case studies and tools that have helped organizations reduce AI risk, accelerate remediation, and strengthen compliance across cloud-native environments.

Whether you’re deploying large-scale models, managing AI data infrastructure, or leading AI governance initiatives, this session equips you with a practical, scalable strategy for building intelligent systems that are not only innovative—but resilient, ethical, and secure by design.