AI Governance Foundations
A practical educational series on AI governance, risk, compliance, security, accountability, and the controls required to govern increasingly capable AI systems.
The series starts with foundational concepts and progressively moves into deeper technical and governance topics.
Modules
Module 01 — What Is AI Governance?
Introduces AI governance from the ground up: authority, accountability, risk control, and evidence.
Module 02 — What Risk Actually Means
Explains risk beyond a single event or score, including residual risk and downstream consequences.
Module 03 — The Compliance Landscape
Explains how NIST AI RMF, ISO/IEC 42001, the EU AI Act, CMMC, and SOC 2 differ, overlap, and relate to governance.
Module 04 — AI Security and the Threat Landscape
Introduces AI security as a systems problem spanning models, data, context, memory, tools, identities, infrastructure, supply chains, and external effects.
Module 05 — Cryptography: What It Does and Why AI Depends on It
Introduces cryptography as the trust machinery surrounding AI systems, including confidentiality, integrity, authentication, digital signatures, secure communications, provenance, and key management.
Module 06 — The Quantum Threat and Post-Quantum Cryptography
Explains what quantum computing actually threatens in modern cryptography, why migration matters before a cryptographically relevant quantum computer exists, and how NIST-standardized post-quantum cryptography changes the trust layer.
Format
Each module currently includes:
- a presentation-ready PDF
- an editable PowerPoint source
- a module-specific README
Publishing Approach
This repository serves as the maintained home for the AI Governance Foundations series.
Future modules and revisions will be added here as the curriculum develops.
Published by Odin's LLC.
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