sicurity.aiTrack D
AI Security Automation Engineer
AI and security automation
PROPOSED GASAE OBJECTIVE ALIGNMENT · NOT VERIFIED COMPLETE EXAM PREPARATION
The person who builds the SOC that runs while nobody is watching
Build AI-assisted security workflows. Test decisions, require approval where it matters, and retain evidence for every action. The proposed GASAE alignment is not verified as complete exam preparation.
AI/ML Security Engineer role: Build safe AI-assisted security workflows with testing, approval gates, rollback, and evidence.
DESIGN PREVIEW / SAMPLE DATA
0 of 13 modules accessible. Availability is confirmed separately from enrollment.
At a glance
- Study time
- 26 to 52 estimated hours, allowing 2 to 4 hours per module. Additional practice varies.
- Modules
- 13 planned modules, including the free foundation
- Credential alignment
- PROPOSED GASAE OBJECTIVE ALIGNMENT · NOT VERIFIED COMPLETE EXAM PREPARATION
- Free module
- Where AI belongs in a SOC. Planned · not accessible yet. Free access depends on release.
- Start date
- Later in the rollout. No start date announced.
Who this is for
Bring your security practice.
Extend it to AI.
Detection engineers who have started writing rules with a model and want to know when to trust them; SOAR and automation engineers whose playbooks can now take actions that hurt; cloud security engineers responsible for automated response in AWS or Azure; purple team leads who want continuous emulation instead of an annual exercise.
You build a fictional company's detection and response automation across AWS, Azure, and on-premises systems. Each week adds a workflow and a controlled test. Later scenarios challenge a triage agent with poisoned telemetry and test overnight automation. GIAC's published GASAE areas include Windows and Linux remediation, host artifact analysis, infrastructure as code, and breach-and-attack simulation. The proposed course plan references these areas; complete exam coverage has not been verified.
Recommended preparation
Recommended: scripting, SOC workflows, Windows and Linux basics, and introductory AWS/Azure knowledge. Foundations helps identify safe automation candidates.
What you will leave with
- An automation charter with blast-radius limits, approval tiers, and rollback
- An evaluation harness for any agent before it touches production
- Hardened baselines and infrastructure templates
- The same playbook in an open-source and a commercial orchestration engine
- Automated detection-to-containment in AWS and in Azure
- Adversary emulation chains and a breach-and-attack simulation plan
- Emulation in the pipeline that fails builds when coverage drops
- A triage agent that survives poisoned telemetry
- A detection promotion gate with precision and recall
- An automation runbook graded by a master
Your capstone
Produce an automation runbook with a safety model, evaluation and promotion gates, rollback procedures, and evidence from an adversarial night-shift exercise.
Inside the labs
One fictional company.
Four connected tracks.
The guide follows a fictional organization with an AI support assistant, a finance agent, coding agents, business-built automations, and security operations across cloud and on-premises systems.
Each week moves from a case file to a practical build, a controlled test, a retained artifact, and readiness work. Allow about five hours per week, roughly 60 hours across the track. Lessons and community run on Skool. The labs run in the sicurity.ai environment shown below. Live-fire and assessment dates are published with each release.
Screens are design previews with sample data, not live lab access or learner results.
DESIGN PREVIEW / SAMPLE DATA
The course range: connected systems, track ownership, and recent activity.
DESIGN PREVIEW / SAMPLE DATA
The weekly sprint: case file, build, controlled test, artifact, and readiness.
DESIGN PREVIEW / SAMPLE DATA
Design preview inspired by GitLab AI Gateway CVE-2026-90970. Trace data, answers, and timer are fictional; this is not evidence of a reproduced exploit.
The course / 12 weeks plus foundations
See the work.
See what it produces.
Each module shows its access status below. Planned modules are curriculum previews, not accessible lessons. Only owner-confirmed releases include a direct Skool module link; Skool sign-in and the required membership may apply.
Open any week for its scenario, learning objectives, and lab interface preview where available. A free foundation label describes planned pricing, not current access.
WEEK 00 / FREEWhere AI belongs in a SOCPlanned · not accessible yet
Free, shared with every track. Where models and agents help in operations, where they fail, and how to tell the difference before production. You leave with an automation candidate list ranked by blast radius.
Learning objectives
WEEK 01The automation charterPlanned · not accessible yet
The case file is a playbook on your instance that quarantines 300 healthy hosts after one bad detection, read with OWASP LLM03 Excessive Agency, which moved to third on the 2026 list. Before you automate anything else you write the rules that bound it: blast-radius limits, approval tiers, rollback, a kill switch. Pass condition: the misfire is capped and reversed inside the limit you set.
Learning objectives
WEEK 02Evaluate the agent before it touches anythingPlanned · not accessible yet
A vendor proposes a triage agent. You build a tool-calling agent of your own and an evaluation harness with pass thresholds, then run the vendor's agent through it and accept or reject it on evidence.
Learning objectives
WEEK 03Baselines and infrastructure as codePlanned · not accessible yet
The range's Windows and Linux baselines have drifted. You write hardening baselines, infrastructure templates, and a disposable test range you can rebuild in minutes, because every later week needs a place to break things.
Learning objectives
WEEK 04One playbook, two enginesPlanned · not accessible yet
Phishing triage has to work in the open-source orchestration engine your team runs and in the commercial one the parent company bought. You build it in both and prove they pass identical test cases, so you leave able to work in whatever your next shop runs.
Learning objectives
WEEK 05AWS, detection to containmentPlanned · not accessible yet
A workload in the company's AWS account is compromised and the evidence must survive. You wire native detection to automated isolation with evidence preservation built in, scored on time to isolate and whether the evidence hash is intact.
Learning objectives
WEEK 06Azure, the same scenarioPlanned · not accessible yet
The identity, mail, and copilots live in Azure. You build automated actions, logging, and rollback there, and learn where the two clouds' response models differ.
Learning objectives
WEEK 07Emulate the adversary that used an agentPlanned · not accessible yet
The case file is GTG-1002 as MITRE catalogued it in Campaign C0062, including the technique "Obtain Capabilities: Artificial Intelligence." You build emulation chains for its tactics with Atomic-style tests and an emulation framework, and a breach-and-attack simulation plan the company can run every month.
Learning objectives
WEEK 08Offense in the pipelinePlanned · not accessible yet
A rule change silently breaks coverage, which is how most detection regressions happen. You put emulation into the pipeline so every change is attacked before it merges, and the build fails when a detection stops firing.
Learning objectives
WEEK 09The agent that read its orders in a logPlanned · not accessible yet
This is a synthetic telemetry-poisoning scenario based on OWASP LLM01:2026 Prompt Injection, not an assertion that no real incidents exist. An alert's user-agent field tries to tell the triage agent to close the alert and suppress the host. You test field isolation, a strict output schema, and tool permissions that prevent suppression. Schemas constrain structure, not the truth of a verdict. Proposed exercise targets are zero changed verdicts across 120 seeded attempts and clean-set accuracy above 0.90; these are not measured results or general guarantees.
Learning objectives
DESIGN PREVIEW / SAMPLE DATA
The triage agent under attack. Alert A-7734 carries an instruction in its user-agent field; field isolation rendered it as data, the output schema rejected free text, and the agent has no suppression tool to misuse. Zero verdicts changed across 120 attempts.
WEEK 10Promote a detection only when the numbers say soPlanned · not accessible yet
A model writes a rule in eight seconds. Your gate replays thirty days of labeled telemetry before it goes anywhere near production and promotes only when precision and recall clear the bar you set. The same week you automate host artifact analysis with Velociraptor and Timesketch-style triage data, because GASAE tests it.
Learning objectives
DESIGN PREVIEW / SAMPLE DATA
The promotion gate. Precision clears 0.90; recall sits at 0.77 against a 0.80 gate because twelve missed events used at.exe and PowerShell scheduled jobs instead of schtasks. The rule is not promoted, and the gate says why.
WEEK 11The night shiftPlanned · not accessible yet
Track C attacks the company overnight, including a poisoned-telemetry attempt against your own triage agent. Your pipeline runs without you. In the morning you read the log: missions completed, false positives, time to contain, what the agent sent to humans and what it decided alone.
Learning objectives
DESIGN PREVIEW / SAMPLE DATA
Live-fire scoreboard: team actions, event timings, and retained evidence.
WEEK 12The runbookPlanned · not accessible yet
The CISO wants the document that explains what the automation does, what it may never do, how it is promoted, and how it is rolled back. You write the automation runbook with its safety model, promotion gates, and rollback procedures, and a master grades it. Then the exam sprint: GASAE simulation, 82 questions in 3 hours, hands-on tasks included.
Learning objectives
Proposed exam objective alignment
Build skills. Verify readiness.
The course plan proposes alignment with GIAC AI Security Automation Engineer (GASAE) objectives. Complete coverage has not been verified. It is not official GIAC courseware, and track completion does not guarantee an exam result.
Published GIAC exam information
GIAC lists one proctored CyberLive exam: 82 questions, 3 hours, and a 70% passing score for versions released on or after April 10, 2026. The completion window is 120 days from activation. Checked October 31, 2026; confirm your attempt's specifications in your GIAC account. Current fees must be checked directly with GIAC.
Confirm current format, fees, eligibility, and objectives with GIAC before booking. Exam fees are separate. Course practice questions are independently authored, not actual GIAC questions. Internal readiness targets are not GIAC passing scores or validated predictors.
Assessment interface preview
DESIGN PREVIEW / SAMPLE DATA
Exam practice: questions, objective coverage, and readiness.
GASAE crosswalk
The ten objective headings match GIAC's GASAE page as checked October 31, 2026. Week assignments and evidence rubrics are proposed by sicurity.ai; heading alignment does not establish complete exam coverage. Primary-source review: October 31, 2026. References are linked below. Scenarios, scores, thresholds, and interface values are fictional course-design examples, not incident measurements or learner results. Crosswalks are sicurity.ai's proposed curriculum alignment, not approval by GIAC, OWASP, or NIST. Exam fees are separate. Confirm current objectives and assessment requirements with the certifying body before booking.
Primary sources
- GIAC GASAE: exam format and ten objective headings ↗
- OWASP Top 10 for LLM Applications 2026: risk names and numbering ↗
- Anthropic: reported AI-orchestrated espionage campaign and estimated 80 to 90% AI contribution ↗
- MITRE ATT&CK Campaign C0062: GTG-1002, September 2025 ↗
OWASP risk names are referenced for identification. Course scenarios and proposed mappings are sicurity.ai interpretations, not official OWASP materials or endorsements.
Evidence / scope / access
Real work.
Clear boundaries.
Hosted labs and live-fire are not currently available. Course releases include access details and setup requirements. Published references have been checked; final labs, assessment validity, and coverage still need verification before assessment enrollment.
Primary-source review: October 31, 2026. References are linked below. Scenarios, scores, thresholds, and interface values are fictional course-design examples, not incident measurements or learner results. Crosswalks are sicurity.ai's proposed curriculum alignment, not approval by GIAC, OWASP, or NIST. Exam fees are separate. Confirm current objectives and assessment requirements with the certifying body before booking.
Start with the free foundation when released
Use the module access list above to check what is accessible before choosing a membership. Enrollment does not imply every module is released.