MLOps & Platform Security

4 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

Vector Database Security: Access Control, Tenant Isolation, Poisoning, and Forensic Logging

Vector database security requires the same seriousness as other production data infrastructure, with additional attention to embeddings, metadata filtering, retrieval authorization, tenant isolation, poisoning resistance, deletion workflows, and forensic logging.

11 min read