The rapid adoption of MLOps has unlocked new levels of innovation, allowing organizations to build, deploy, and maintain machine learning models efficiently. However, these advantages come with security challenges that are often underestimated, leading to risks such as data breaches, model theft, and unauthorized access. Securing MLOps infrastructure on AWS requires a holistic approach, extending beyond traditional cloud security practices to address the unique needs of machine learning workflows.

In this session, we’ll explore often overlooked but critical security considerations for MLOps environments. We’ll discuss strategies for protecting sensitive training data, securing model artifacts in storage, and implementing fine-grained access control across AWS services like SageMaker, S3, Lambda, and Redshift. Emphasis will be placed on securing data pipelines, handling PII securely and employing robust encryption methods for data at rest and in transit.

We’ll also tackle overlooked areas such as securing third-party integrations, preventing data poisoning attacks, and monitoring for malicious model behavior using AWS-native tools like GuardDuty, CloudWatch and Detective. Real-world case studies and practical examples will illustrate best practices and pitfalls to avoid.

Attendees will gain actionable insights on how to safeguard their MLOps workflows, ensuring not only model performance but also data integrity and trustworthiness. Whether you're a security professional, machine learning engineer, or cloud architect, this presentation will equip you with the knowledge to build resilient and secure MLOps solutions on AWS.