Every enterprise is deploying AI. Few have a governance framework that will survive an audit.
The EU AI Act is in effect. The SEC expects AI risk disclosures. Your board wants to know who is responsible when an AI system makes a consequential decision. “We are using ChatGPT” is not a governance strategy.
I help enterprises build AI governance frameworks that are practical enough to implement and rigorous enough to satisfy regulators. Here is the structure that works.
The Four Pillars
1. Model Inventory and Risk Classification
You cannot govern what you do not know exists. Start with a complete inventory:
# AI Model Registry Entry
model_id: "REC-001"
name: "Product Recommendation Engine"
owner: "ML Platform Team"
business_unit: "E-Commerce"
risk_tier: "Limited" # Minimal | Limited | High | Unacceptable
model_type: "Collaborative Filtering + LLM Reranking"
training_data: "Customer purchase history (2 years), product catalog"
pii_exposure: "Yes - customer IDs, purchase patterns"
decision_type: "Automated recommendation, no human override required"
deployment: "Kubernetes, 4x A100"
last_audit: "2026-02-15"
next_audit: "2026-08-15"
eu_ai_act_category: "Limited risk - transparency obligations"Risk tiers aligned with the EU AI Act:
| Tier | Examples | Requirements |
|---|---|---|
| Unacceptable | Social scoring, real-time biometric ID in public | Prohibited |
| High | Credit scoring, hiring, medical diagnosis | Full conformity assessment |
| Limited | Chatbots, recommendation engines | Transparency obligations |
| Minimal | Spam filters, search ranking | No specific obligations |
Most enterprise AI falls into Limited or High risk. Know which tier each system belongs to before regulators ask.
2. Data Governance and Lineage
Every AI model inherits the biases and limitations of its training data. Track:
- Data sources — where did training data come from?
- Data freshness — when was it last updated?
- PII handling — what personal data is included, how is it protected?
- Consent basis — under what legal basis was this data collected?
- Bias assessment — does the data represent all relevant populations fairly?
Data Lineage for Credit Scoring Model:
────────────────────────────────────────
Source: Customer transactions (ERP)
→ ETL: Anonymization pipeline (PII stripped)
→ Storage: Data lake (encrypted at rest)
→ Feature engineering: 47 features extracted
→ Training: XGBoost + LLM explanation layer
→ Validation: Fairness metrics across demographics
→ Deployment: Kubernetes inference endpoint
→ Monitoring: Drift detection, bias alerts3. Monitoring and Accountability
Production AI systems drift. Models degrade. Biases emerge over time. Monitor:
- Performance metrics — accuracy, precision, recall tracked weekly
- Fairness metrics — demographic parity, equalized odds, disparate impact ratio
- Drift detection — data drift and concept drift alerts
- Explanation logging — for high-risk decisions, store the reasoning
- Human override rate — how often do humans override the AI’s recommendation?
Accountability matrix:
| Role | Responsibility |
|---|---|
| AI Ethics Board | Policy, risk appetite, escalation decisions |
| Model Owner | Model performance, bias monitoring, compliance |
| Data Owner | Data quality, lineage, consent management |
| ML Engineer | Technical implementation, testing, deployment |
| CISO | Security, access control, incident response |
| DPO | GDPR/privacy compliance, DPIA coordination |
4. Audit and Documentation
Regulators and auditors want evidence. Maintain:
- Model cards — standardized documentation for each AI system
- Impact assessments — Data Protection Impact Assessments (DPIAs) for high-risk AI
- Decision logs — for consequential automated decisions (credit, hiring, medical)
- Change management — version control for models, data, and configurations
- Incident records — AI failures, bias incidents, and remediation steps
Implementation Roadmap
Quarter 1: Inventory all AI systems, classify risk tiers, assign owners Quarter 2: Implement monitoring for high-risk systems, create model cards Quarter 3: Deploy fairness metrics, establish AI Ethics Board, run first audit Quarter 4: Automate compliance reporting, integrate with CI/CD pipelines
Related Resources
- EU Cyber Resilience Act
- AI Agent Security on Kubernetes
- Zero Trust Architecture
- Microsoft Copilot Enterprise Guide
- M365 Enhanced Data Encryption
About the Author
I am Luca Berton, AI and Cloud Advisor. I help enterprises build AI governance frameworks that satisfy regulators and enable innovation. Book a consultation.
