Complete Track - AI Security Engineering - L2

AI Security Engineering L2 Track

A completed SecureTheCloud Labs intermediate track for learning how to engineer secure AI systems with prompt boundaries, tool permissions, retrieval controls, runtime guardrails, abuse controls, testing harnesses, and evidence packages.

StatusComplete Track

LevelIntermediate / L2

Modules9 of 9

RuntimeRead-only course

Start Here

This track should be taken after the AI Governance Command Center Track. AI Governance teaches how to decide whether an AI workflow should be allowed, blocked, approved, escalated, or audited. AI Security Engineering teaches how to design AI systems so those controls are enforceable and testable.

AI Security Testing and Evidence Harness Teach AI security test cases, failure-path evidence, prompt/tool/retrieval/output/runtime checks, and audit-ready validation packages. AI Abuse, Cost, and Rate Limit Engineering Teach token budgets, rate limits, quota controls, repeated attempts, abuse patterns, throttling, denial, and evidence capture. AI Runtime Guardrails and Failure Mode Engineering Teach loop control, retries, timeouts, fail-closed behavior, circuit breakers, degradation, escalation, and evidence capture. Output Safety and Response Policy Engineering Teach response classification, sensitive data handling, grounded answers, refusal behavior, escalation, and unsafe-response prevention. Retrieval Security Engineering Teach secure retrieval design for source authority, tenant scope, sensitivity, freshness, poisoning resistance, and evidence capture. Tool Permission Engineering Teach scoped tool permissions, action classification, mutating vs non-mutating tools, approval gates, and self-approval prevention. Prompt Boundary Engineering Teach how to separate system instructions, developer instructions, user input, retrieved content, and tool outputs. Secure AI Application Architecture Teach secure architecture patterns for AI apps, including frontend/backend separation, model boundary, API boundary, policy boundary, and evidence boundary. Prerequisite Track Start with AI Governance Command Center before beginning this engineering sequence. Learning Path Reference Review the SecureTheCloud Labs starting point and learner flow. AI Track Sequence Review the intended AI learning sequence across Governance, Engineering, and Red Team tracks.

Available Modules

The first AI Security Engineering module is now available.

AI Security Engineering Overview Introduce secure AI system design, core threats, control layers, and engineering boundaries.

Track Modules

Completed modules are linked as LAB pages. Upcoming modules remain planned until they are implemented, verified, and gated.

Prerequisites

Learners should complete the AI Governance Command Center Track first.

Recommended prerequisite:
AI Governance Command Center Track

Required concepts:
- AI governance command center purpose
- risk tiering
- policy gates
- human approval
- agent workflow governance
- prompt injection
- tool hijacking
- RAG data boundaries
- audit evidence
- cost and rate-limit governance

Track Relationship

The AI Security Engineering track is the next step after AI Governance Command Center and is followed by the completed Cloud Security Operations L2 Track.

AI Governance Command Center
-> AI Security Engineering
-> Cloud Security Operations
-> Portfolio-ready progression

Governance question:
Should this AI workflow be allowed, approved, blocked, escalated, or audited?

Engineering question:
How do we design the AI workflow so those controls are enforceable and testable?

Track Boundary

This track is a static read-only completed learning path. It does not create live controls.

Track complete = true
LAB modules implemented = 9 of 9
Backend exposure = false
Live model integration = false
Live tool execution = false
Live retrieval execution = false
Live approval workflow = false
Provider quota mutation = false
Runtime mutation = false
Production enforcement claim = false