aidriven threat detection

Guide to hardening a Linux KVM/QEMU/libvirt hypervisor for AI/GPU workloads with bash-ready steps: keep hosts minimal and patched, enable IOMMU and use VFIO for PCIe/GPU isolation, enforce OVS + nftables VLAN segmentation, add vTPM + UEFI Secure Boot and optional SEV/TDX, encrypt disks, use TLS for migrations, audit and validate, reset/rotate GPUs, and use MIG/SR-IOV for safer multi-tenancy without losing performance.

A practical guide to bolting a small, auditable AI layer onto Linux logs with journald, Bash pipelines, and a tiny Python Isolation Forest that learns normal SSH patterns, flags anomalies, and alerts via Slack/syslog; includes cross-distro installs, train/stream scripts, systemd service and weekly retrain timer, tuning guardrails, and extensions to sudo, kernel, packages, and containers.

A Bash-first, privacy-safe playbook for AI-assisted Linux security audits: baseline with Lynis/OpenSCAP, enable auditd/AIDE/journald, export logs to redacted JSON, detect outliers locally via Isolation Forest, optionally use a local LLM for remediation summaries, and automate via cron—triaging noisy events, surfacing anomalies, and prioritizing high-impact fixes without sending data to the cloud.