Artificial Intelligence is transforming industries, but with its power comes significant security risks. AI workloads in the cloud face unique challenges, including adversarial attacks, data poisoning, and model theft. This session delves into securing AI in cloud environments, covering key strategies for protecting AI models, securing APIs, and ensuring data integrity.

We’ll explore the threat landscape for AI, including attack vectors such as model inversion, prompt injection, and unauthorized access to training data. The session will also cover secure AI deployment by integrating security into the AI lifecycle using MLOps and DevSecOps principles.

Attendees will learn how to apply Zero Trust principles to AI workloads, implement identity and access controls, encrypt data pipelines, and comply with security frameworks like FedRAMP, GDPR, and NIST AI Risk Management Framework.

By the end of this session, participants will understand best practices for deploying secure AI solutions in AWS, GCP, and Azure, mitigating AI-specific risks, and strengthening their organization’s AI security posture.