Udemy - Threat Hunting in GovCloud - AI-Assisted SecOps Masterclass torrent

Threat Hunting in GovCloud: AI-Assisted SecOps Masterclass
https://WebToolTip.com
Published 9/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 20h 38m | Size: 886.78 MB
From scattered alerts to a governed, evidence-driven SOC — telemetry, threat intel, AI triage, and SOAR, all built by yo
What you'll learn

Architect a sovereign SecOps platform from zero

Engineer multi-source telemetry pipelines

Build detection-as-code

Operate a full threat intelligence lifecycle

Govern AI in the SOC with hard boundaries

Automate response safely with SOAR-style case management

Secure Kubernetes runtime environments

Prove operational resilience

Map every control to real frameworks

Deliver a capstone-grade Sovereign AI-Assisted Threat Hunting and Automated Response Platform
Requirements

Knowledge: Basic Linux command-line comfort (navigating folders, running scripts). No prior SOC, threat hunting, or security operations experience required — Module 1 builds everything from a working workstation up. Basic Python reading ability helps (most generators and detections are short scripts) but isn't required; every script is explained line by line. No prior Kubernetes, Terraform, or AI/LLM experience needed — Modules 6 and 8 build those skills from scratch using deterministic stubs and a local Kind cluster. Software (all free/open-source): A Linux workstation or VM (Ubuntu 24.04 LTS recommended), 8 CPU cores and 16GB RAM recommended, 80GB+ free disk space. Docker and Docker Compose, Git, Python 3.12, jq, yq, make. kubectl, kind, terraform, helm for the Kubernetes and infrastructure modules (all free, no cloud account required). Open-source security tooling used via local install or containers: OPA, YARA, Falco (via Helm), MinIO — no licenses or paid platforms required. Optional: a local LLM runtime (e.g., Ollama-compatible) for Module 6 — the course works fully with deterministic AI stubs if you'd rather skip model downloads. Hardware: No cloud account, no real government or production infrastructure, and no real personal or classified data required — every lab uses synthetic identities, synthetic telemetry, and a local Kind Kubernetes cluster.