MLSecOps framework maps 10 security pillars for production ML systems
goyalshaliniuk · x · 2026-09-02
As AI systems move to production, security—not accuracy—is the biggest threat. This framework lays out MLSecOps, combining ML engineering, cybersecurity, MLOps, and governance across the full pipeline:
- Model hardening via adversarial training
- Dataset integrity to catch poisoning and anomalies
- Data security & governance with access control
- MLOps integration for continuous security testing and CI/CD protection
- Supply chain security for model files and dependencies
- Audit, compliance & logging
- Explainability & transparency for bias detection
- Secure deployment: endpoint auth, encrypted serving
- Monitoring & drift detection
- Threat detection: model extraction, inversion attacks, prompt injection, API abuse
The author argues MLSecOps is now foundational for trustworthy AI in production.
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