ai governance

A hands-on guide to operationalizing AI governance on Linux with Bash and stock tools: isolate workloads via rootless containers, systemd slices, and ACLs; prove integrity with GPG-signed checksums; enforce guardrails with OPA policy-as-code and no-network runs; audit files and executions using auditd; pin image digests and log run manifests for reproducibility—aligned with NIST/EU/ISO, no heavy platforms required.

Practical, Linux-first playbook to turn AI pilots into enterprise-grade systems: install a minimal Bash-friendly toolchain (apt/dnf/zypper), standardize with venv + pinned deps + Podman, version data/models via Git+DVC+S3 for lineage, build in security (GPG-encrypted secrets, hashes, rootless containers), add observability/cost control with simple logs/benchmarks, and automate quality with pre-commit and CI.

Practical Bash-first guide to assessing AI workloads on Linux: inventory models, datasets, code, containers and GPUs; generate SBOMs with Syft and scan vulns with Grype; lint Python with Bandit and hunt secrets with Gitleaks; harden Podman runtimes (read-only, no-new-privileges, controlled egress) and monitor with auditd and checksums; keep a JSON risk register; automate in CI/cron for repeatable, regulatory-ready assurance.