MLSecOps: A Comprehensive Framework for Secure Machine Learning Operations

Abstract

This article introduces MLsecOps as an integrated framework combining machine learning operations with safety ideas to solve the particular flaws of the ML system throughout its development life cycle. Through rigorous verification methods, negative testing, and ongoing monitoring, MLSecOps offers methodical defense against data poisoning, negative attacks, and model drifts. Companies utilizing MLSecOps, compared to conventional safety solutions, experience security phenomena, fast threats, and superior models experience a substantial reduction in flexibility. Stating how MLsecops safeguards security systems against, the framework's efficacy is demonstrated by case studies in autonomous vehicles, e-commerce suggestions, and healthcare diagnostics. This helps AI to be accountable in high-day scenarios by extending DevSecOps concepts. Applications where safety breakdowns could have major repercussions.