LEARNING PLAN: The 5-Layer Master Protocol for Enterprise AI Security

Target Identity: AI Security Authority & Integrated Architect Unfair Advantage: I turn AI security theory into enterprise architectural frameworks that actually work in enterprise environments.

Phase 1: Governance & Model Layers (The Foundation)

Goal: Secure the “Core” and the “Rules” (Layers 1 & 4).

• Governance Layer (L4):
  • Deep dive: ISO/IEC 42001:2023 and NIST AI RMF.
  • Study: EU AI Act compliance for “High-Risk” systems.
  • Prep for IAPP AIGP certification.
• Model Layer (L1):
  • Mastery of Training Data Integrity and Model Evaluation.
  • Practical: Jailbreak Resistance and simulated Red-Teaming exercises.
• Output: Publish “The Leader’s Guide to ISO 42001 Architectural Mapping.”

Phase 2: Interaction & System Layers (The Pipeline)

Goal: Secure the “Flow” and the “Integration” (Layers 2 & 3).

• Interaction Layer (L2):
  • Mastery: Prompt Injection (direct/indirect) and Input Filtering.
  • Implementation: Output Guardrails and Contextual Validation gateways.
• System Layer (L3):
  • Deep Dive: RAG Security (Vector DB access) and Agentic Risk.
  • Architecture: Tool Permissioning, API Governance, and Agent Orchestration.
• Output: Create the “Apex Architecture: A Reference Model for Secure Agentic RAG.”

Phase 3: The Integrity Layer & Board Synthesis

Goal: Secure the “Reality” of behavior and ROI (Layer 5).

• Integrity Layer (L5):
  • Master Behavioral Anomaly Detection and Model Drift detection.
  • Monitor: Guardrail Performance and production telemetry.
• Board-Level ROI:
  • Metrics: Combine Layer 1-4 telemetry into a single “Integrity Score”.
  • Strategy: Translate “Model Drift” into Business Continuity Risk.
• Output: Launch the “Board-Ready AI Integrity Report” template.

Continuous Integration (Weekly Rituals)

• Deep Work: 90 minutes of “Pattern Recognition” (research papers/threat reports).
• Ship Weekly: One high-signal authority piece (LinkedIn/Blog).
• Pipeline Lab: 1 hour/week building/breaking local RAG & Agent instances.

“I build systems that survive reality.”

Key Insights

The opportunity is real and growing Companies are rapidly deploying AI-powered applications without proper security testing, creating significant vulnerability exposure and career opportunity for those who learn this skill.

The learning path (beginner to entry-level)

1. Gandalf (gandalf.lakera.ai) — learn the basics of prompt injection
2. Agent Breaker — hack realistic AI-powered apps (portfolio advisor, trip planner, etc.)
3. Auto Parts CTF — a self-hostable CTF based on a real client pen test, with 5 flags to capture

Free resources exist — Jason’s team at CANAM open-sourced 23 AI hacking labs, available on GitHub Pages.

What real AI hacking looks like From the Auto Parts demo: a simple search bar leaked a system prompt, exposed API keys, and then revealed confidential patent data, pricing, and corporate secrets from a RAG database — all from prompt injection alone.

LLMs are non-deterministic — the same attack may need to be tried 5–10+ times, which is fundamentally different from traditional hacking.

The barrier to entry is low — a 12-year-old completed the Auto Parts CTF in 35 minutes. Completing it puts you at the end of entry level.

Monetisation paths — bug bounties (Anthropic, OpenAI, Google all have programs), CTF cash prizes, and job opportunities in a still-emerging field.