AI systems introduce new attack surfaces that traditional security models were not designed to handle. From data poisoning to prompt injection, and from supply-chain vulnerabilities to model drift, AI pipelines demand a new security mindset.

This session examines the evolving threat landscape around AI infrastructure and presents practical defense strategies, including:

Securing training and inference pipelines in cloud-native environments

Detecting and mitigating model drift in production

Protecting against data poisoning and prompt injection attacks

Applying zero-trust principles to AI systems

Integrating AI security into DevSecOps workflows

Designed for engineers and security leaders, this talk offers concrete architectural guidance for building AI systems that are not only intelligent—but resilient and secure.