digital forensics

Hands-on guide to AI-assisted digital forensics on Linux: assemble a reproducible Bash/Python toolchain; extract auditable file features (size, MIME, SHA-256, strings density); triage at scale with an Isolation Forest; combine YARA rule hits with ML for unknown-weird; add fast PCAP flow triage via tshark—all local, scriptable, and court-ready to speed prioritization without sacrificing chain-of-custody rigor.

Shows how to fuse battle-tested Bash with a tiny, offline ML model to speed Linux incident response: a one-command triage bundle (processes, network, auth, SUID, hashes), an Isolation Forest to rank unusual processes from procs.csv, a quick SSH auth outlier scorer, and systemd automation. Includes install steps, runnable scripts, and guardrails (baseline, least-privilege, audit tuning), cutting noise and surfacing real leads fast.

Practical, Bash-first guide to AI-assisted incident response on Linux: collect a reproducible triage bundle, summarize and prioritize findings locally with Ollama (no data off-host), harden detection via auditd, and sweep for IOCs with ClamAV and YARA. Includes apt/dnf/zypper installs, redaction, JSON-ready AI prompts, and a real-world example to drive rapid, privacy-preserving, triage-driven decisions.