The Problem: "Private" ≠ "Secure" We’re all moving toward self-hosted AI platforms like Ollama and LocalLLMs to protect proprietary code and internal workflows. But here’s the uncomfortable reality: Most local AI deployments are nothing more than security theater . If your stack is running: An unauthenticated Redis instance Containers without syscall isolation AI-generated code directly on the host kernel …then your infrastructure is still exposed. A single SSRF (Server-Side Request Forgery) vulnerability can provide attackers lateral access to internal services, secrets, and execution environments. What Exactly Is "Hardening"? In modern DevSecOps , Hardening is the process of minimizing a system’s attack surface by removing insecure defaults and enforcing strict isolation policies. Instead of deploying a "default install," we harden every layer of the AI stack.…