AI Governance Examples
Governance frameworks and structures for responsible AI deployment — oversight committees, risk assessment processes, ethical guidelines, and compliance monitoring.
AI Governance Committee Structure
intermediateA governance structure with a cross-functional AI steering committee, clear roles and responsibilities, decision-making authority, and regular review cadence for AI initiatives across the organisation.
Key takeaway: Effective AI governance committees include business, legal, technical, and ethics representatives — technology-only committees miss critical perspectives.
AI Risk Assessment Framework
intermediateA structured framework for assessing AI project risks across dimensions of bias, safety, privacy, reliability, and reputational impact, with risk tiers that determine required oversight levels.
Key takeaway: Risk-tiered governance (low/medium/high) prevents over-governing low-risk AI use cases while ensuring appropriate scrutiny for high-risk applications.
AI Model Evaluation and Approval Process
advancedA stage-gate process for evaluating, testing, and approving AI models before production deployment, including bias testing, accuracy validation, security review, and performance benchmarking.
Key takeaway: A formal approval process for AI models prevents deploying systems that work in demos but fail in production — the gap between prototype and production is where most AI projects fail.
AI Incident Response Plan
advancedA plan for responding to AI-related incidents (harmful outputs, bias detection, data breaches, system failures) including escalation procedures, communication templates, and post-incident review processes.
Key takeaway: AI incidents require faster response than traditional IT incidents because of reputational risk — have communication templates and escalation paths ready before you need them.
AI Ethics Guidelines for Practitioners
beginnerPractical ethical guidelines for developers and product managers building AI features, covering fairness, transparency, accountability, and human oversight requirements with concrete decision-making criteria.
Key takeaway: Ethics guidelines that include specific decision criteria ('if X, then do Y') are followed more consistently than abstract principles.
Third-Party AI Vendor Assessment
intermediateA framework for evaluating third-party AI vendors and services covering data handling practices, model transparency, compliance certifications, SLA requirements, and contractual protections.
Key takeaway: Vendor AI assessments should focus on data handling, model update notification, and exit strategy — these are the areas where vendor lock-in and risk concentrate.
Patterns
Key patterns to follow
- Risk-tiered governance prevents both over-governing and under-governing AI use cases
- Cross-functional governance committees (business, legal, technical, ethics) make better decisions than single-function teams
- Practical guidelines with specific decision criteria are followed more consistently than abstract principles
- Pre-built incident response plans and communication templates reduce response time when issues arise
FAQ
Frequently asked questions
AI governance is the set of policies, processes, and structures that ensure AI is developed and used responsibly. It covers risk management, ethical guidelines, compliance, accountability, and oversight to ensure AI systems are safe, fair, and effective.
Yes, but scale it appropriately. Small companies need basic policies (acceptable use, data handling, vendor assessment) and simple review processes. You do not need a full governance committee — a checklist and designated reviewer can suffice until you scale.
AI governance builds on data governance — data quality, privacy, and security are prerequisites for responsible AI. Extend your existing data governance to cover AI-specific concerns like model bias, explainability, and automated decision-making.
The EU AI Act is the most comprehensive AI regulation, requiring risk assessments and governance for high-risk AI systems. GDPR covers automated decision-making. Various industry regulators (FCA, FDA) have AI-specific guidance. The regulatory landscape is evolving rapidly.
Start with three things: an AI acceptable use policy for employees, a simple risk assessment checklist for new AI projects, and a designated person responsible for AI governance. Build more sophisticated processes as your AI usage grows.
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