AI Governance Framework Implementation
AI Governance is not bureaucracy for bureaucracy's sake. It's a structure ensuring that AI systems in your company operate predictably, safely, in accordance with organizational values and regulatory requirements. The absence of governance becomes a problem at the first incident.
AI Governance Framework Components
AI Inventory: Registry of all AI systems in the organization: what they do, what data they use, who is responsible, risk level (high/medium/low). Without inventory — impossible to manage.
Risk Classification: EU AI Act (if applicable) divides AI into unacceptable risk, high risk, limited risk, minimal risk. Similar classification is built for all systems. High-risk requires documentation, testing, human oversight.
Model Documentation (Model Cards): For each production ML model: purpose, training data, metrics, limitations, known biases, contraindicated use cases. Google Model Cards standard.
Fairness and Bias Auditing: Regular audit of production models for discrimination based on protected attributes. Fairlearn (Microsoft), AI Fairness 360 (IBM). Mandatory for systems affecting decisions about people.
Data Governance: Where data comes from, how labeled, what consent obtained, retention period. GDPR/CCPA compliance for personal data in ML pipeline.
Incident Response: Procedure for AI incidents: classification, escalation, investigation, remediation, public disclosure if necessary. Who decides to shut down the system.
Monitoring & Review: Periodic review of AI systems. What changed in data, in the world, in regulatory environment — does risk need reassessment.
Regulatory Context
EU AI Act (entered into force 2024, phased implementation 2025–2027). GDPR Art. 22 (automated decisions). Industry requirements: finance (SR 11-7), healthcare (FDA AI/ML guidance), banking (BCBS 239).
Development Pipeline
Weeks 1–3: AI Inventory. Risk classification.
Weeks 4–7: Policy development (AI use, data, third-party AI services). Model documentation templates.
Weeks 8–12: Review and approval processes for new AI systems. Employee training.
Weeks 13–16: Monitoring setup. Incident Response playbook. Annual review schedule.
Delivery
AI Governance Policy, Model Documentation Templates, Risk Assessment Framework, Incident Response Playbook, Training Materials, Compliance Checklist.