model hardening

Outlines security best practices for running AI locally: strict data governance and encryption, least-privilege access, model sandboxing, signed/verified models and dependencies, robust secrets management, and reproducible builds. Emphasizes defenses against prompt injection, continuous monitoring and audit logs, timely patching, air-gapped setups, and policy-driven governance to protect privacy and compliance.