risk assessment

Practical, bash-first guide to securing AI models and pipelines on Linux: distro-ready installs (apt/dnf/zypper), venvs with hash-pinned deps and CVE audits, secret hygiene with git-secrets and sops/age, artifact hashing/signing (SHA256, minisign), sandboxed runtimes (firejail, bwrap, rootless Podman), image/file scans via Trivy, a copy-paste checklist script, and real-world tips to cut supply-chain and runtime risk.

AI stacks evolve fast and widen attack surfaces across models, Python deps, and containers. This Bash‑first Linux playbook shows how to inventory assets and publish SBOMs with Trivy, continuously scan Python and images, sandbox inference rootless with Podman, lock down egress and envs, and verify model provenance via checksums/pinning—complete with copy‑paste apt/dnf/zypper installs and CI/cron automation.

Comprehensive guide for full stack developers and sysadmins on using Bash to orchestrate AI-based risk assessment: set up Python/virtualenv and ML libs, automate data handling, train models via scripts and cron, run scheduled inference, log and alert on failures, and report results, with best practices for modularization, version control, and security plus curated resources.