The label has no universal standard definition; useful implementations combine machine learning operations, secure development, AI risk management, and operational security rather than naming a single team or tool.
MLSecOps treats datasets, code, models, prompts, configurations, evaluation artifacts, registries, pipelines, serving infrastructure, and external components as governed assets. It adds security tests and evidence to delivery workflows while maintaining production monitoring, response, recovery, and feedback to developers and model owners.
Key points
Lifecycle protectionEstablish provenance and access controls, isolate build and evaluation environments, scan dependencies, sign artifacts, protect registries, and separate duties for approval and release.
Threat evaluationEvaluate poisoning, evasion, extraction, privacy leakage, unsafe outputs, dependency compromise, excessive tool access, and misuse under realistic system and user contexts.
Operations and responseMonitor data and model drift, abuse, anomalous access, performance and security signals; preserve evidence; support rollback, revocation, incident response, and safe retirement.
Important limitationAdding security gates to an MLOps pipeline does not make an AI system secure or trustworthy. Unknown failure modes, weak evaluations, compromised data, unsafe application logic, and post-deployment context can bypass pipeline checks.