SecMLOps: A comprehensive framework for integrating security across the MLOps lifecycle

The SecMLOps framework embeds security measures across the MLOps lifecycle, defending against complex attacks such as adversarial attacks at every stage from design and deployment to continuous monitoring. The framework demonstrates practical application with a pedestrian detection system (PDS) use case, and empirical evaluation reveals trade-offs between security measures and system performance, providing guidance for balancing security and efficiency in ML deployment.