A guide for CTOs and DevSecOps engineers on hardening local AI deployments. Just because it’s local doesn’t mean it’s secure.
Key Sections:
1. **Threat Vectors:** Prompt injection, model theft, training data poisoning.
2. **Network Security:** Air-gapping requirements, mTLS for inference usage.
3. **Access Control:** Implementing API keys and usage quotas for internal LLM APIs.
4. **Audit Logs:** Logging prompts and completions (without violating privacy policies).
5. **Sanitization:** Input/Output guardrails using tools like Guardrails AI.
**Internal Linking Strategy:** Link to Pillar. Link to ‘Deploying to Kubernetes’.
Continue reading
Enterprise Local AI: A Security & Compliance Checklist
on SitePoint.
