AI governance is the organizational framework for directing and overseeing how AI is designed, deployed, and used.
By setting policy, accountability, and decision rights, and by covering ethics, compliance, risk appetite, lifecycle controls, and ongoing oversight across people, process, and technology, AI governance enables organizations to deploy AI responsibly and at scale.
Focus areas
AI governance addresses several key areas:
Accountability and ownership
Define clear ownership for AI systems, including use case owners, business sponsors, and oversight committees responsible for decisions about AI deployment.
Acceptable use and ethical constraints
Establish policies governing what AI can and cannot be used for, including ethical boundaries, prohibited applications, and acceptable risk levels.
Human oversight and operational controls
Implement controls ensuring human involvement in AI-driven decisions, especially for high-risk or consequential outcomes.
Compliance and regulatory readiness
Align AI usage with applicable regulations such as the EU AI Act, ensuring documentation, transparency, and audit readiness.
Lifecycle controls for deployment and change
Govern how AI systems move from development to production, including intake processes, approval gates, and change management.
Typical artifacts
AI governance programs produce documentation that demonstrates oversight:
Use case inventories — Centralized registries of AI systems and their purposes
Impact assessments — Evaluations of potential risks and harms
Approval records — Documentation of governance decisions and sign-offs
Oversight logs — Evidence of ongoing monitoring and human review
Using ValidMind for AI governance
ValidMind supports AI governance workflows through a unified platform:
flowchart TB
center((ValidMind<br>AI Governance<br>Platform))
inv((Inventory))
wf((Workflows))
docs((Documents<br>& Templates))
reg((Regulations<br>& Policies))
dep((Dependencies))
life((Lifecycle))
lib((Content<br>Library))
center --- inv
center --- wf
center --- docs
center --- reg
center --- dep
center --- life
center --- lib
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Inventory
Captures record types, defines custom fields, and identifies stakeholders.
Workflows
Manages review cycles, approval paths, and assigned tasks.
Documents & Templates
Provides standard forms, reports, and documentation templates.
Regulations & Policies
Includes internal policies, external regulations, and compliance frameworks.
Dependencies
Maps data sources, LLM relationships, and interconnected systems.
Lifecycle
Governs development, deployment, monitoring, and retirement stages.
Content Library
Offers reusable components, a knowledge base, and additional resources.
Model your AI inventory
Configure primary record types to match how your organization actually manages AI — whether that’s an entire AI systems, the underlying large-language models (LLMs), use cases, tools, applications, data assets, or a combination thereof:
Define custom record types beyond just large-language models.
Add fields for risk classification, ownership, and business context.
Capture the metadata that matters for your governance program.
Include relationships and dependencies
Create typed relationships between records to map how AI components connect and depend on each other:
Link AI systems to underlying LLMs, data sources, and applications.
Visualize dependencies across your AI portfolio.
Inherit properties and requirements through relationships.
Drive governance based on change and materiality
Use version-aware workflows that differentiate between major and minor changes, triggering appropriate validation and approvals:
Configure workflows that respond to change severity.
Automate approval routing based on materiality.
Track version history with full audit trail.
Ready to implement AI governance?
Explore our implementation guides for specific regulatory frameworks: