Secure AI/ML pipelines from the start
This whitepaper introduces a practical, visual framework for integrating security across the machine learning lifecycle. Built for practitioners, it draws on proven DevSecOps strategies and adapts them for AI/ML environments.
Who Should Read This
- AI/ML engineers, data scientists, and MLOps teams
- Developers and cloud-native professionals incorporating AI/ML
- Security engineers and IT teams expanding governance to ML systems
- Open source contributors in the AI/ML security space
What’s Inside
- Visual models mapping MLOps and MLSecOps lifecycles
- Key risks, controls, tools, and personas across stages
- Open source guidance using frameworks like Sigstore, OpenSSF Scorecard, and SLSA
- Real-world recommendations for securing ML systems end-to-end
Why Now
AI adoption is accelerating—and so are the risks. From model theft to data poisoning, traditional software security practices aren’t enough. MLSecOps is the next step in securing machine learning systems across their lifecycle.
Get Involved
Read the full whitepaper, join the AI/ML Security Working Group, and explore OpenSSF membership opportunities.
