Phase 8: Production AI Security Architecture · 5-6 hours · dataclasses · enum · argparse
Project: Production AI Security System
Every phase in this course built one control. This is where they stop being separate lessons and become one system that runs.
Hiring signal: This is the artifact an AI Security Architect or Staff AI Security Engineer interview loop is actually trying to find: not someone who can name all seven layers of a reference architecture, but someone who can wire architecture validation, guardrails, monitoring, CI/CD gates, supply chain scanning, and incident response into one system that runs end to end against a real target app and produces evidence, not slides. Companies hiring for production AI security roles (Anthropic, Microsoft AI Red Team, and any enterprise shipping customer-facing LLM features) evaluate exactly this: can you make the controls talk to each other.
What you will learn
- Wire a 7-layer architecture validator, a multi-attack-category guardrail pipeline, a 3-signal security monitor, CI/CD security gates, a supply chain scanner, and an incident response generator into one orchestrator that runs against a single target application
- Produce evidence-based deliverables (a scored architecture report, a guardrail test matrix, a monitoring dashboard, a gate report that actually fails on a bad change, an SBOM, and an incident playbook) rather than descriptive documentation
- Map every implemented control to the NIST AI RMF's Govern/Map/Measure/Manage functions as compliance documentation a reviewer or auditor could act on
- Demonstrate, with a reproducible before/after comparison, that a CI/CD security gate blocks a deployment when a known-bad change (disabled injection detection, an unresolved supply chain finding) is introduced
The Problem
Look back at what's accumulated since Phase 0. A threat model. An OWASP LLM Top 10 mapping. A prompt injection detector and a jailbreak library. Adversarial robustness evaluations. A RAG and agent security audit. A guardrail pipeline. A full red-team engagement with Garak and PyRIT. A governance mapping to NIST AI RMF and the EU AI Act. And in this phase alone: a 7-layer architecture validator, a security monitor with three real signals, CI/CD security gates, a supply chain scanner, and an incident response playbook generator.
Every one of those is real, working code. None of them, on their own, is what a hiring panel means when they ask "have you built a production AI security system." That question is really asking something more specific: can these pieces talk to each other? Does the architecture score actually inform what the guardrail pipeline blocks? Does an incident detected by the monitor actually map to a playbook that names the right CI/CD gate to re-run? Does the compliance documentation reflect what's actually implemented, or is it aspirational?
This is the course's flagship deliverable for exactly that reason. The rubric doesn't ask for a diagram of a 7-layer architecture — it asks for "concrete, working implementations" at all 7 layers. It doesn't ask for a guardrail pipeline described in prose — it asks for one that "demonstrably blocks at least 2 attack categories from earlier phases." It doesn't ask for a CI/CD pipeline described as a YAML snippet — it asks for gates that "actually fail the build on a known-bad change." Every rubric line is a verb: covers, blocks, surfaces, fails, maps. This lesson builds the orchestrator that does all five, against one real target application, in one run.
Assembling the 7-Layer Architecture for a Real Target App
The target application for this capstone is support-copilot: a customer-support AI assistant with RAG retrieval over a support-ticket and product-docs index, and two tool calls with real side effects — send_email and issue_refund. This is a deliberately realistic choice: it's exactly the shape of system that shows up in Phase 4's RAG/agent security material and Phase 2's injection material, which means every attack category earlier phases taught you to find has a concrete home here.
The capstone runs SecurityArchitecture.validate() — the same validator from Lesson 1 — against two configs for this app: its current, under-secured state (TLS and an API gateway, but no injection detection, no output filtering, no tamper-evident audit logs) and a fully hardened target state. The scored, per-layer gap report between those two isn't decoration — it's the artifact a real design review produces, and it's what tells you exactly which control to build next if you were doing this against your own target application instead of the synthetic one here.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Wiring Up a Monitoring Dashboard with Real Security Signals, Building CI/CD Security Gates for the Target Repo, Writing the Incident Response Playbook and Compliance Documentation, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.
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