The MLSecOps imperative
In Chapters 3, 5, and 6, our discussions about securing AI-driven systems explored the roles of DevSecOps and MLOps. DevSecOps emphasizes integrating security practices within software’s development and operations life cycle, advocating a security as code philosophy. This approach ensures that security measures are not afterthoughts but are ingrained throughout the development process.
MLOps, conversely, plays a critical role in managing the life cycle of machine learning models, from data preparation and model training to deployment and monitoring, emphasizing automation and continuous improvement. MLOps provides an invaluable platform for good governance, which aids security but – reflecting the emerging nature of threats – has not had the maturity emphasis on security that DevSecOps brought to DevOps.
Early MLSecOps approaches consisted of tentative but encouraging steps in bringing security tooling and practices into MLOps pipelines...
