Model and Provider Risk

5 articles

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Map

The AI Security Buyer’s Guide: How to Evaluate Vendors for LLM Firewalls, Guardrails, Evals, and Monitoring

AI security buyers should judge vendors by the job to be done: filtering, testing, evals, access, logs, leaks, rules, and proof. Choosing a vendor should start with design and risk, not just labels.

9 min read

Defend

Secrets Management for AI Apps: API Keys, Model Providers, Tool Credentials, and Delegated Access

AI applications need disciplined secrets management across model provider keys, vector stores, tool credentials, OAuth tokens, browser sessions, cloud keys, notebooks, logs, prompts, and agent runtimes. Secure design requires centralized secret storage, short-lived and scoped credentials, delegated authorization, redaction, rotation, revocation, and incident-ready evidence.

10 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