Securing AI Pipelines from Development to Production
summary
Claims a 73% reduction in breaches after adopting zero trust across ML pipelines, quantifies three top threats (data poisoning 95, interface interference 90, model theft 85), and shows practical controls: sandboxed training zones, least-privilege identities, digitally signed models stored in an audited model registry. Implements runtime hardening and automated anomaly detection with tools like Okta, HashiCorp Vault, AWS Secret Manager, Snyk/Trivy/Dependabot, Istio/Linkerd, Falco, DVC and MLflow, and reports real-world compliance wins and a 40% drop in manual audit time.
AI-written summary · about
chapters (15)
- 0:00Introduction and Speaker Background
- 0:23The New Battlefield: ML Ops Security Challenges
- 1:53Agenda Overview
- 2:35Defining the Core Problem
- 3:52Hidden Threats in ML Ops
- 5:03Introducing Zero Trust
- 6:21Implementing Zero Trust in ML Lifecycle
- 7:04Stage 1: Securing Data Preparation
- 8:08Stage 2: Model Training and Validation
- 9:32Stage 3: Deployment and Serving
- 10:43Stage 4: Monitoring and Governance
- 12:46Real-World Impact and Case Studies
- 13:57Actionable Steps to Begin
- 15:05Tools and Common Pitfalls
- 17:17Core Takeaways and Conclusion
related talks
22mBuilding Secure AI Platform Infrastructure: A Zero Trust Framework
26mRunning Secure AI/ML Experiment in a Unified MLOPS-DevSecOps Pipeline
18mCommit Secure AI: Embedding Security in Code, Pipelines, and Production
23mTrust, Track, and Verify: Securing AI Pipelines End-to-End
43mThe art of secure software: Zero-trust delivery pipelines
50mBuilding AI Security In: MLSecOps in Practice
30mSecuring AI Workloads: Building Zero-Trust Architecture for LLM Applications
39mSecuring AI at Scale: Practical Defenses against Prompt...