AI Supply Chain

5 articles

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Defend

Notebook Security for ML and AI Teams: Jupyter, Colab, Databricks, and Hidden Execution Risk

Notebook security for AI and ML teams requires access control, secret management, data minimization, execution isolation, output review, dependency scanning, sharing controls, provenance, and promotion rules before notebooks influence production workflows or access sensitive data.

9 min read

Defend

Cloud Security for AI Workloads: GPUs, Secrets, Buckets, Model Endpoints, and Notebook Risk

Cloud security for AI workloads requires inventorying AI assets, protecting model endpoints, securing GPU and notebook environments, managing secrets, locking down object storage and vector stores, scanning containers, limiting egress, monitoring cost, and integrating AI infrastructure into normal cloud security operations.

10 min read

Defend

LLMOps Security: CI/CD, Secrets, Eval Gates, Model Registry Controls, and Deployment Promotion

LLMOps security requires CI/CD controls for prompts, tools, model configuration, provider routing, evals, secrets, registries, deployment promotion, monitoring, rollback, and governance evidence. AI release processes must track every artifact that can change system behavior.

10 min read

Defend

Securing Open-Source Models: What to Check Before Running a Model in Production

Open-source models require a production intake process covering provenance, license review, file formats, remote code, unsafe serialization, dependencies, containers, evals, serving infrastructure, monitoring, rollback, and governance evidence.

11 min read

Defend

Model Supply Chain Security: From Hugging Face to Docker Images to Fine-Tuned Weights

The model supply chain is now a real security boundary. Teams pull weights, adapters, datasets, containers, and prompts from many places. Without provenance, the release path becomes impossible to trust.

3 min read