A Gentle Introduction to MLSecOps
summary
Defines a concrete ML security threat model (supply‑chain compromise, data exfiltration, membership inference) and prescribes measurable MLSecOps practices: versioning data and models, saving predictions and comparing statistics (mean, std, word frequencies), keeping model provenance logs and an ML bill of materials, running end‑to‑end tests, and deploying continuous monitoring with anomaly detection and incident response. References US 2021 executive action and EU Cyber Resilience/AI guidance, mentions an IBM open‑source toolkit, and treats privacy‑by‑design and GDPR compliance as verifiable requirements rather than vague goals.
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chapters (10)
- 0:00MLOps trilogy intro
- 1:47ML lifecycle
- 6:27MLSecOps vs DevOps
- 10:24ML SecOps definition
- 13:20ML supply chain risks
- 16:13Model provenance & ML BOM
- 18:23Reliable AI & fairness
- 20:29Adversarial ML security
- 21:54Responsible ML principles
- 24:46ML SecOps takeaways
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