AI Governance Engineer
GitHub

The discipline on one page.

Every chapter, layer, pattern, workflow, obligation, maturity level and learning stage the site covers, as one map of links. Start at the branch you need and follow it into the text.

Body of Knowledge · v0.5.0

New to the field? Read what AI governance covers before you follow a branch.

A central node and eight branches.

Four branches on the left, four on the right, each with its second-level topics and, where they fit, third-level chips: every one a link into the text.

Scroll sideways, or use the list below.

The map of the discipline A two-sided mind map with a central AI Governance Engineer node and eight branches, each with its second-level topics as links. Foundations The definition The three questions The disambiguation cluster AI safety research MLOps / LLMOps Model risk management (SR 11-7 style) AI compliance / legal Responsible AI / AI ethics GRC engineering AI security engineering The five problems, with the evidence Governance written Point-in-time review Governance as a gate Framework theatre No runtime data path Values & principles The eight values The six principles The Stack Layer 01 Govern-as-Code Layer 02 Inventory & Transparency Layer 03 Evals & Red Teaming asEvidence Layer 04 Runtime Controls &Observability Layer 05 Assurance & ContinuousCompliance The minimum viable stack for a team ofone Patterns Layer 01 Govern-as-Code Policy Card FRIA-as-Code Framework Crosswalk Use-Case Intake & Risk Tiering AI Threat Model Dataset Admission Gate Layer 02 Inventory & Transparency Agent Registry AIBOM Model Card as Control Evidence Shadow-AI Discovery Vendor / Model Due-Diligence Gate Training-Data Rights Ledger Model Artefact Integrity Rights Requests Against Models Downstream Use Register Layer 03 Evals & Red Teaming asEvidence Eval Gate in CI Adversarial Red-Team Suite Fairness Eval Suite Layer 04 Runtime Controls &Observability Runtime Guardrail Kill Switch / Circuit Breaker Agent Identity & Scoped Credentials Human-in-the-loop Gate Explanation Artefact Decision Notice & Contest Path Sanctioned AI Gateway Staged Rollout with Rollback Criteria Drift & Fairness Monitor Retirement Runbook Layer 05 Assurance & ContinuousCompliance Continuous Assurance Telemetry Incident Pipeline Machine-Readable Evidence (OSCAL) Claims Substantiation Gate Disclosure & Notification Pipeline The Role Intake and classification Inventory and registry Evals and red teaming as evidence Policy-as-code and gates Runtime monitoring and incidents Assurance and audit evidence Regulatory translation The career ladder Three ways in From Legal or privacy From Security or GRC From MLOps or ML engineering Obligations Risk management Topic crosswalk Risk management Governance and accountability Impact assessment Data governance Documentation and transparency Inventory and registration Logging and traceability Human oversight Runtime guardrails Robustness, security and evaluations Incident response and monitoring Supply chain and third parties Prohibited practices Fairness and non-discrimination Privacy and data protection Explainability and right to explanation AI literacy and competence Conformity assessment and certification GPAI and foundation models IP and copyright Agent identity and autonomy Content provenance and deepfakes Sandboxes and real-world testing Environmental impact Deployment, change and decommissioning Frameworks EU AI Act, post-Omnibus GPAI Code of Practice Data protection and other EU law ISO/IEC 42001, 42005 and 42006 NIST AI RMF CSA AICM and STAR for AI OWASP GenAI Security Project US federal and state laws Other jurisdictions Obligation → artefact → layer Maturity 1 Documented 2 Inventoried 3 Tested 4 Enforced 5 Continuous Self-assessment checklist Metrics per level Learning path Foundations See it and rule it Test it and contain it Prove it and specialise AI GovernanceEngineer
The map of the discipline The whole discipline on one canvas: a central node and eight branches (foundations, values, the stack, patterns, the role, obligations, maturity and the learning path), every node a link. Follow the branch you are weakest in, or read the same map as a list below. Drawn from chapters 01–08. Permalink, downloads and citation
Text description

A two-sided mind map. The centre is the AI Governance Engineer. Four branches sit on the left: Foundations (the definition, the three questions, the disambiguation cluster, the five problems), Values and principles, The Stack (five layers and the minimum viable stack) and Patterns (the catalogue by layer); and four on the right: The Role (seven workflows, the career ladder, three ways in), Obligations (the topic crosswalk, the frameworks and the reverse index), Maturity (the five levels, metrics and the self-assessment) and the Learning path (four stages of nodes). Every node links to the page or on-page anchor that develops it; the list under the map is the same content as text.

Every node is a link. The list below is the same map as text.

Content by cluster.

Foundations DefinitionWhy Now

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

Values & principles

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

The Stack

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

Resources

Patterns

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

The Role

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

Resources

Obligations Regulatory Map

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

Resources

Maturity Maturity Model

In the map

Chapter sections

Diagrams and figures

Learning-path nodes

Learning path

In the map

Learning-path nodes

Resources

Cite or download this map · SVG, and PNG in light and dark, rendered from Body of Knowledge v0.5.0