Threat Hunting at Machine Speed AI SecOps Bootcamp

What you'll learn

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Engineer a replayable telemetry pipeline
Build detection-as-code across signal types
Govern AI in the SOC with real guardrails
Automate response the safe way
Operate SecOps like a production system
Prove reliability with metrics
Implement governance as code
Document compliance readiness
Design for sovereignty and federation
Deliver a capstone-grade Sovereign AI-Powered SecOps and Automated Response Platform
Requirements

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Knowledge: Basic terminal comfort (navigating folders, running commands). No prior SOC, SIEM, or SOAR experience required - Module 1 builds everything from a bare workstation up. Basic Python reading ability helps (every lab script is short and explained) but isn't required. No prior Kubernetes, Docker, or CI/CD experience needed - Module 6 builds those skills from a local Kind cluster and Docker Compose. Software (all free/open-source): Docker and Docker Compose, Git, Python 3.12, make. kubectl and kind for the Module 6 Kubernetes labs - a lightweight local cluster, not a cloud account. Python packages installed via pip in Lab 4: FastAPI, Uvicorn, Pydantic, PyYAML, requests, pandas, scikit-learn, pytest - all free and open-source. Optional: YARA binary for Module 3 (a grep-based fallback is provided if you'd rather skip the install). Hardware: 10GB+ free disk space, 8GB+ RAM recommended (Docker containers plus an optional local Kubernetes cluster running concurrently). No cloud account, no real production SIEM, no real personal or customer data, and no paid AI API required - every lab uses synthetic telemetry and deterministic AI stubs so the entire AI-assisted workflow works without spending a cent on model calls.
Description

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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 AI Will Run the SOC" Fantasy
There's a seductive pitch going around security operations right now: hand your alerts to an AI, and it will triage, investigate, and respond faster than any human team. It's also a dangerous oversimplification. An unsupervised AI system making security decisions without evidence grounding, schema validation, or human approval isn't acceleration - it's a new, harder-to-audit failure mode. The real, defensible answer isn't "no AI" or "AI runs everything." It's AI as a bounded accelerator: schema-validated output, evidence-bound retrieval, hallucination checks, and mandatory human approval before anything consequential happens.
That's exactly what this course builds, lab by lab, alongside a full production-grade detection and response pipeline. Nothing here is theoretical - every AI output is validated against a JSON schema, every response action fails closed without explicit approval, and every workflow is tested before it's trusted.
The Solution: 100 Labs, One Growing SecOps Platform
Every lab follows the same Zero-Failure framework: a pre-flight check, a Git-based safety net, and a documented rollback. By Lab 10 you'll have a complete mini pipeline - synthetic events generated, normalized, detected, reported, and tested - proving the full alert-to-evidence loop works before you add any complexity. Everything after that adds real production depth.
What's Inside
-Modules 1-2 build your workstation and a genuinely replayable telemetry pipeline: synthetic event generation, schema validation with Pydantic, an event replay harness for deterministic testing, IP reputation and geo/ASN enrichment from local feeds, deduplication, retention policy, OpenSearch bulk export, and pipeline health metrics - because detection engineering on bad data is worse than no detection at all.
-Module 3 builds real detection-as-code: Sigma-style YAML rules converted into working local runners, unit tests that catch broken detections before they ship silently, ATT&CK mapping with a coverage report, YARA file-pattern practice, Suricata-style network rules, and osquery-style endpoint hunts - all four major detection surfaces, tested and working together by the module milestone.
-Module 4 is where this course earns its name: AI-assisted hunting built entirely on governance rails. A structured triage prompt template that forbids unsupported certainty, an evidence packet builder that bounds AI to real reports (never open-ended memory), a deterministic output schema, a hallucination/claim-support guard that checks AI statements against actual evidence, query-safety classification separating read-only from approval-required actions, a lightweight embedding stub, and - critically - a measurable evaluation harness with analyst feedback capture, so AI quality becomes a tracked metric, not a vibe.
-Module 5 builds SOAR the responsible way: YAML playbooks with dry-run execution before anything real happens, Cortex-style observable analyzers, case creation and enrichment attachment, a response-action registry that classifies risk and requires approval, fail-closed approval enforcement (the pipeline literally blocks itself without explicit sign-off), staged blocklist generation with a documented rollback plan, and SOAR reliability metrics measuring dry-run success ratio.
-Module 6 turns the whole system into a production platform: a containerized FastAPI service, Docker Compose orchestration, a local Kind Kubernetes cluster, Services and ConfigMaps/Secrets separation, OpenTelemetry-style trace IDs propagated through the API, readiness/liveness endpoints, load testing against the alert-ingestion endpoint, and backup/restore for evidence - real reliability engineering applied to a real SecOps pipeline.
-Module 7 closes the governance gap most courses skip: an explicit RBAC role model, Rego-style policy-as-code with tested allow/deny logic (default-deny, naturally), an SBOM placeholder, a container-check CI workflow, GDPR breach-triage and DORA resilience evidence templates, a dedicated AI risk register mapping specific risks to specific controls, chaos-tested API restart recovery, and an incident postmortem template that focuses on systems, not blame.
-Module 8 finishes with sovereignty and federation: a documented sovereign deployment topology (local telemetry, local AI inference where possible, no raw personal data leaving the boundary), data classification labels, an evidence redaction pipeline, a federated indicator-sharing bundle with confidence scoring and expiry/decay, provenance-tracked research notes, and offline packaging that explicitly excludes secrets and private data.
The Climax: Lab 100 - The Sovereign AI-Powered SecOps and Automated Response Platform
Lab 100 runs the entire system end to end: telemetry generation and normalization, Sigma-style detection, AI-bound triage with schema validation, case creation, analyzer enrichment, artifact collection, fail-closed approval enforcement, SOAR run logging and metrics, evidence redaction, and sanitized indicator sharing - all verified by a capstone readiness script that checks every required artifact exists before the final tag is created. This isn't a certificate exercise. It's proof that you built a system where AI accelerates the work and humans remain firmly in control of every consequential decision.
Why Enroll Now
The market signal here is unambiguous: the strongest security operations job descriptions aren't asking for "AI SOC operators" - they're asking for engineers who can automate responsibly, with detection-as-code discipline, measurable AI governance, and production-grade reliability. That's a specific, learnable skill set, and this course is one of the only places that teaches it end-to-end without requiring a paid AI API, a commercial SIEM license, or a cloud account. Every tool here is free and open-source - build it once, and it's yours to run, extend, and put directly into a portfolio that proves engineering discipline, not just tool familiarity.
Who this course is for

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The SOC Analyst Ready to Move Into Detection Engineering You triage alerts in a commercial SIEM every day, but you've never built a detection from raw telemetry to tested, ATT&CK-mapped rule. This course builds that entire pipeline yourself - Sigma-style rules, YARA, Suricata-style network detection, and osquery-style hunts - so you walk away able to explain and defend detection logic, not just click through a vendor console.
The Security Engineer Skeptical of "AI-Powered SOC" Marketing You've seen the pitch decks promising an autonomous AI analyst, and you know that's not how production security should work. This course builds AI-assisted triage with exactly the guardrails that should exist: deterministic output schemas, evidence-bound retrieval, a hallucination/claim-support checker, human approval gates, and a measurable evaluation harness - all without requiring a paid API, since the AI layer runs on deterministic stubs you fully control.
The Platform/DevOps Engineer Being Asked to Own SecOps Infrastructure You can containerize and deploy services, and now you're expected to run the security stack too. This course gives you the exact production patterns for SecOps specifically: Kubernetes deployment, health/readiness endpoints, chaos-tested restart recovery, load testing, backup/restore, RBAC, and Rego-style policy-as-code - all applied to a real detection-and-response pipeline you build from scratch.
Homepage

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