zero trust

Hands-on guide to Zero Trust for AI on Linux: assume breach, verify all artifacts (models/packages/images), run with least privilege via rootless Podman or Firejail, lock down files/secrets with strict perms, auditd/inotify and sops, deny outbound by default (--network none, UFW), and observe with eBPF/bpftrace; includes apt/dnf/zypper installs, systemd hardening, real-world mitigations, and a week-one action checklist.

AI Meets Bash shows how to secure Linux AI workloads with pragmatic, Bash-first guardrails: scan and sanitize datasets (ClamAV, jq), block secret leaks and audit access, vet Python deps (pip-audit, bandit), run minimal rootless containers (Podman), harden inference with systemd and firewalld, and add ML-driven SSH anomaly alerts, with apt/dnf/zypper install steps, copy-paste commands, and actionable next steps.