# What is Machine Learning Security Operations (MLSecOps)?

> Machine learning security operations (MLSecOps) is an emerging practice that integrates security responsibilities and controls into machine learning development and operations across data preparation, model development, evaluation, release, deployment, monitoring, change, and retirement.

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- Language: en
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## Content

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 protection:** Establish provenance and access controls, isolate build and evaluation environments, scan dependencies, sign artifacts, protect registries, and separate duties for approval and release.
- **Threat evaluation:** Evaluate poisoning, evasion, extraction, privacy leakage, unsafe outputs, dependency compromise, excessive tool access, and misuse under realistic system and user contexts.
- **Operations and response:** Monitor data and model drift, abuse, anomalous access, performance and security signals; preserve evidence; support rollback, revocation, incident response, and safe retirement.
- **Important limitation:** Adding 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.

### Related terms

[DevSecOps](<https://yellowcube.eu/glossary/devsecops/>) · [AI security](<https://yellowcube.eu/glossary/ai-security/>) · [Continuous integration and continuous delivery or deployment (CI/CD) pipeline security](<https://yellowcube.eu/glossary/continuous-integration-and-continuous-delivery-or-deployment-pipeline-security/>) · [Data poisoning](<https://yellowcube.eu/glossary/data-poisoning/>) · [Model poisoning](<https://yellowcube.eu/glossary/model-poisoning/>)

### Sources

[NIST SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models](https://csrc.nist.gov/pubs/sp/800/218/a/final) · [NIST, Secure Software Development Framework](https://csrc.nist.gov/projects/ssdf) · [NIST, AI RMF Playbook](https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook)

## Attribution and scope

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