AI governance is the decision rights, evidence, controls, and accountability used to manage an AI system throughout its lifecycle. It should address whether a system should exist—not only whether a model performs well. Risk can arise from data, models, interfaces, operations, suppliers, security, or context.
NIST’s AI Risk Management Framework organizes voluntary outcomes into Govern, Map, Measure, and Manage. These continuous, context-sensitive functions are not a certification or universal checklist. Organizations must map applicable law separately and strengthen controls as potential impact increases.
Scope note: This guide is not legal advice. Duties depend on jurisdiction, sector, organizational role, intended use, and system risk.
1. Assign accountable decision rights
A committee can help, but it is only one design. Allocate responsibility across business ownership, engineering, security, privacy, legal, procurement, operations, and affected-domain expertise. Inventory systems, owners, suppliers, uses, affected groups, data classes, and deployment status. Define who may approve, pause, roll back, or retire a system; record evidence, dissent, exceptions, residual risk, and review dates. A committee’s existence is not evidence that oversight works.
2. Assess impact before deployment and material change
Document purpose, decision context, affected people, benefit, foreseeable misuse, alternatives, limitations, rights and safety impacts, human workflow, security, contestability, and residual risk. Begin during design and repeat after material change or evidence of harm. Canada’s Algorithmic Impact Assessment applies within the scope of its federal Directive on Automated Decision-Making; it is not a universal certification. The EU AI Act assigns different duties by system category and actor. Confirm applicability and dates with qualified counsel.
3. Operate a risk-management lifecycle
- Map: purpose, users, affected people, dependencies, misuse, alternatives, and limits.
- Measure: predefined tests, relevant groups, uncertainty, security, and acceptance criteria.
- Manage: prioritize risk, implement controls, document residual risk, and prepare rollback.
- Govern: assign owners, resources, training, supplier controls, incident reporting, and challenge.
Generative AI reviews may also cover confabulation, harmful content, privacy and intellectual-property exposure, prompt injection, data poisoning, cybersecurity misuse, and component-integration risk.
4. Govern data, provenance, and evidence
Document sources, collection authority, licenses, transformations, versions, quality rules, representativeness limits, access, retention, and deletion. Lineage can identify contributing datasets and transformations, but generally cannot trace an output to specific training records, explain causality, or establish compliance. Version datasets and pipelines; test label validity, leakage, duplication, missingness, subgroup coverage, and temporal shift. See data access governance.
5. Monitor the complete system
Monitor availability, latency, inputs, outputs, abstentions, overrides, complaints, outcomes, security events, and relevant group performance where lawful and feasible. Input drift is not concept drift, and neither proves degradation. A drift alert should trigger diagnosis—not automatic retraining. Define delayed-label evaluation, incident severity, ownership, escalation, safe state, notification, evidence retention, and post-incident review. See AI model management.
6. Design transparency for its audience
An affected person, operator, validator, and auditor need different evidence. LIME and SHAP are post-hoc attribution methods; they are not causal explanations and do not prove fairness or correctness. Validate fidelity, stability, robustness, comprehensibility, and usefulness. Provide intended and prohibited uses, evaluation summaries, limitations, and a contact path while protecting personal and security-sensitive data. The LLM evaluation metrics guide is a starting point, not a complete governance test.
7. Make human oversight effective
A reviewer must have competence, evidence, time, authority, and a workable way to disregard, override, reverse, or interrupt the system. Measure overrides, error discovery, delay, consistency, complaints, and outcomes—not approval counts. Design against automation bias, fatigue, and rubber-stamping; separate appeals where independence matters.
Minimum lifecycle evidence
| Stage | Evidence | Decision |
|---|---|---|
| Intake | Owner, purpose, affected people, alternatives, data, jurisdiction, risk tier | Reject, explore, or assess |
| Design | Impact assessment, data plan, threat model, recourse | Approve conditions |
| Validation | Reproducible tests, limitations, residual risk | Go, conditional go, or no-go |
| Operation | Monitoring, complaints, incidents, outcomes, changes | Continue, restrict, roll back, or pause |
| Retirement | Dependencies, notice, records, deletion, replacement | Decommission and verify |
Start with an inventory and one use case. Scale only after realistic failure exercises show that controls and recourse work. Connect these practices through an AI governance framework.
Originally published July 27, 2025; technically and legally reviewed September 4, 2026.
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