The AI Security Governance Framework

Security controls and incident runbooks only work if they exist within a governance structure that assigns accountability, enforces standards, and ensures ongoing compliance. This final lesson ties the technical content of Course 1 into a practical governance framework.

The Five Pillars of AI Security Governance

• ◆Inventory & Classification — Know every AI system you operate, what data it processes, who has access, and what the business impact of failure would be. No governance programme can function without this.
• ◆Security Requirements by Risk Tier — Not every AI system needs the same level of control. Define minimum security requirements based on the risk classification of each system — high, medium, or low — and apply them consistently.
• ◆Pre-Deployment Review — Every AI system that goes into production should pass a security review before launch. This review should cover all the threat categories from this course: adversarial robustness, data provenance, supply chain, and (for LLMs) injection resistance.
• ◆Continuous Monitoring — Security is not a point-in-time state. Define what will be monitored for each system, at what frequency, and what thresholds trigger an investigation.
• ◆Incident Response & Learning — Defined runbooks, clear ownership, and a post-incident review process that feeds findings back into your pre-deployment review criteria.

Where to Start

If you're starting from zero, here is the most practical sequence:

• ◆Week 1 — Build your AI system inventory. One spreadsheet, every AI system, key risk attributes.
• ◆Week 2 — Classify each system by risk tier using the framework from this course.
• ◆Week 3 — Identify your highest-risk system and conduct a security review using the threat categories from Course 1.
• ◆Month 2 — Draft your first incident runbook for the most likely threat to your highest-risk system.
• ◆Month 3 — Establish baseline monitoring for your top three systems.