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Threat Hunting in GovCloud AI-Assisted SecOps Masterclass
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.
Description
This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
The "Our SOC Just Collects Alerts" Problem
Most security operations centers have a familiar failure mode: dashboards full of alerts, a SIEM full of noise, and no coherent path from "something happened" to "here's the evidence, here's the response, here's the proof it was handled correctly." Add government or regulated-cloud constraints - data sovereignty, tenant isolation, audit trails, FedRAMP-adjacent expectations - and the gap between "we have a SIEM" and "we have a defensible, evidence-driven SecOps program" gets even wider. Layer AI on top without governance, and you get a new, worse problem: an unsupervised system that can hallucinate conclusions, leak sensitive context into prompts, or take destructive action with no audit trail.
This course closes that entire gap, deliberately and in order. AI is taught as a controlled component - bounded, logged, evidence-grounded, and always subordinate to human approval - never as an autonomous analyst. Every architectural decision is made with sovereignty and compliance as first-class constraints, not an afterthought bolted on before an audit.
The Solution: 100 Labs, One Growing Sovereign Platform
Every lab follows the same Zero-Failure framework: a pre-flight check, a Git-based safety net, and a documented one-step rollback. By Lab 10 you'll have generated synthetic telemetry, written your first hunt query, codified it as a Sigma-style detection, and closed the loop with a documented case and response decision - a complete detect-to-respond cycle in miniature. Everything after that adds real production depth.
What's Inside
-Modules 1-2 build your workstation and a genuine sovereign reference architecture: a landing zone with security, workload, evidence, and break-glass zones; tenant and mission boundaries; network segmentation; least-privilege IAM modeling; and OPA-enforced policy guardrails - all before a single detection is written.
-Module 3 engineers real multi-source telemetry: synthetic cloud audit logs, endpoint process events, network connections, identity authentication, and application audit trails, normalized into a consistent schema, validated with JSON Schema, and measured for data-quality coverage - because detection engineering on bad data is worse than no detection at all.
-Module 4 builds detection-as-code properly: MITRE ATT&CK mapping, failed-login burst detection, suspicious role-assumption hunts, YARA file-pattern matching, a maintained detection catalog with owners and severities, unit tests that stop broken rules from shipping silently, and allow-list-based false-positive tuning that's explicit and reviewable rather than hidden in ad hoc queries.
-Module 5 operates a full threat intelligence lifecycle: a structured IOC store, network enrichment, STIX-like relationship modeling connecting indicators to ATT&CK techniques, an ATT&CK-enriched hunt priority queue, IOC expiry and revocation, a local intelligence API, confidence scoring, and a threat knowledge graph.
-Module 6 is the course's core differentiator: AI-assisted SecOps built with real guardrails from day one - an explicit AI-use policy separating "may summarize/draft/explain" from "may never delete/block/revoke/close," a prompt-logging schema with hashed inputs and outputs, evidence-grounded retrieval instead of unsupported model memory, a deterministic AI-triage stub so you can build the entire workflow without a paid API, AI-drafted Sigma rules quarantined behind mandatory human review, and explicit approval gates that block destructive action by default.
-Module 7 builds SOAR the right way: a case-management schema, enrichment and notification playbooks, containment requests that require explicit approval and carry expiry and rollback plans, an approval-state evaluator that enforces this as executable policy rather than memory, and response-quality metrics - all running in simulation mode, never live destructive automation.
-Module 8 extends security into runtime: a local Kind Kubernetes cluster, synthetic mission workloads with tenant labels, Kubernetes audit simulation, secret-access detection, real Falco runtime behavioral detection validated with a safe test trigger, NetworkPolicy segmentation, OPA-enforced namespace labeling, and hash-verified container forensics.
-Module 9 proves the platform is production-worthy, not just architecturally sound: defined SLOs, tested backup and restore drills, immutable hash-chained evidence, a GDPR-safe log redaction tool, DORA-style resilience evidence, and a full NIST CSF 2.0 control mapping across Govern, Identify, Protect, Detect, Respond, and Recover - plus CI validation that runs your detections and data checks automatically.
-Module 10 closes with sovereignty and honesty: offline platform packaging, a tool bill of materials, a decentralized CTI research workflow with quarantine-before-promotion for shared intelligence, multi-tenant evidence separation, and an Architecture Decision Record that states, in writing, what the lab does and does not prove - including that it is not a real FedRAMP authorization boundary.
The Climax: Lab 100 - The Sovereign AI-Assisted Threat Hunting and Automated Response Platform
Lab 100 runs a full simulated intrusion - logging disabled, a role assumed, data exported, suspicious egress, and Kubernetes secret access - through the entire system you've built: telemetry correlation, AI-bounded triage, a SOAR case with approval-gated containment requests, rollback records, and a final report mapping every piece of evidence to NIST CSF outcomes. The capstone package is hashed, indexed, and - critically - honest about its residual risks. This isn't a certificate exercise. It's a working, defensible architecture you could walk a security review board through line by line.
Why Enroll Now
The market signal here is specific and growing: government and regulated-cloud security roles increasingly expect engineers who can design end-to-end telemetry-to-response systems with real compliance awareness - not just SIEM query familiarity. And as AI enters SecOps workflows everywhere, the engineers who can buildgoverned AI-assisted automation - not unsupervised "AI SOC analyst" hype - are the ones who'll be trusted with production systems. Every tool in this course is free and open-source (OPA, Falco, YARA, Kind, MinIO), so what you build is entirely yours, reproducible, and directly portable to a real regulated environment.
Who this course is for
The Aspiring Threat Hunter or Detection Engineer You've read about MITRE ATT&CK and Sigma rules, but you've never built a detection pipeline end-to-end - from raw telemetry to a tested, owned, false-positive-tuned rule. This course builds that entire path yourself, using open-source tools, so you walk away with a real detection-engineering portfolio instead of a certificate for watching demos.
The Security Engineer Wary of "AI Will Run Your SOC" Hype You've seen the vendor pitches about AI-powered SecOps and you're skeptical - rightly so. This course treats AI exactly as it should be treated: a bounded assistant for summarization, drafting, and anomaly scoring, with mandatory prompt logging, evidence grounding, and human approval before anything consequential happens. You'll build the governance rails yourself, not just take them on faith.
The GRC-Aware Platform/Security Architect in Regulated Environments You work in or around government, financial, or critical-infrastructure systems where FedRAMP, NIST CSF, DORA, NIS2, and GDPR aren't optional footnotes - they're the job. This course treats compliance as engineering evidence from Lab 1: tenant isolation, data classification, encryption ownership, hash-chained evidence, and a full NIST CSF 2.0 control mapping, culminating in a capstone architecture you could genuinely defend to a review board.
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