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Learning Paths

Structured learning paths for AI governance

Don’t know where to start? Follow a path. Each is an ordered set of guides that takes you from the fundamentals of AI agents and identities to securing MCP, enforcing policy, and proving compliance.

Path 1 · AI Governance Fundamentals Beginner
1

What is an Enterprise AI Control Plane?

The big picture: why AI needs its own governance layer alongside Okta and Entra, and what a control plane does.

 2

Understanding AI agents

What agents are, how they act autonomously, and why they behave differently from traditional software.

 3

AI identities & the registry

Treating every agent, model and tool as a governed identity you can name, own and revoke.

 4

AI models in the enterprise

How models fit the governance picture — provenance, ownership and risk.
Path 2 · Securing AI Agents & MCP Intermediate
1

MCP servers & tools

How the Model Context Protocol connects agents to systems — and why it’s a critical control point.

 2

AI policies & authorization

Deciding what each AI identity may do, and enforcing it at the moment of every tool call.

 3

AI sessions & runtime control

Making autonomous activity observable and revocable in real time.

 4

Apply OWASP for AI

Map the top LLM & agentic risks — prompt injection, excessive agency — to concrete defenses.
Path 3 · Compliance & Audit for AI Advanced
1

AI audit & evidence

Building the immutable record of what every AI identity did — the foundation of accountability.

 2

AI compliance

Mapping AI activity to SOC 2, ISO 42001 and the EU AI Act.

 3

Frameworks & standards deep-dive

NIST AI RMF, ISO/IEC 42001 and the EU AI Act — what each requires and how to satisfy it.

 4

Reference: the glossary

Keep the vocabulary handy as you operationalize governance across teams.

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