The EU AI Act entered full force in February 2026. If your company uses AI in hiring, credit decisions, healthcare, or any other high-risk application — and you operate in or sell to the EU — you need a compliant AI governance framework or face fines up to €30M or 6% of global annual revenue. Here's how to build one.
Understanding the Risk Tiers
The EU AI Act classifies AI systems into four risk tiers: Unacceptable Risk (banned), High Risk (strict requirements), Limited Risk (transparency obligations), and Minimal Risk (no requirements). High-risk applications include: AI in hiring and HR decisions, credit scoring and lending, healthcare diagnosis and treatment, critical infrastructure, law enforcement, and educational assessment.
The Four Pillars of an AI Governance Framework
1. AI Inventory and Risk Classification: Document every AI system in use — including third-party tools — and classify each by risk tier. Many companies are surprised to discover they're using high-risk AI through SaaS tools they didn't build themselves.
2. Bias and Fairness Monitoring: High-risk AI systems must be continuously monitored for discriminatory outcomes across protected classes. This requires automated bias detection that runs on every model output and flags disparate impact before it accumulates into legal exposure.
3. Explainability and Documentation: Every high-risk AI decision must be explainable — you must be able to tell a person why the AI made a decision about them. This requires implementing XAI (Explainable AI) techniques like SHAP values and maintaining documentation of model architecture, training data, and performance metrics.
4. Human Oversight Mechanisms: High-risk AI systems must have human oversight — a human must be able to review, override, or stop any AI decision. Document these override procedures and train staff on when and how to exercise them.
The Audit Trail Requirement
The EU AI Act requires that high-risk AI systems maintain logs sufficient to enable post-hoc auditing of decisions. At minimum, log: the input data, the model version, the output/decision, the timestamp, and the human reviewer (if applicable). These logs must be retained for at least 10 years for some applications.
Third-Party AI Compliance
If you use a third-party AI tool for a high-risk application, you're still responsible for compliance. You must obtain documentation from the vendor, conduct your own bias testing on your specific use case, and implement your own monitoring. "Our vendor is compliant" is not a sufficient defense.
Implementation Timeline
A realistic AI governance implementation for a mid-size company takes 3–6 months: Month 1 for AI inventory and risk classification, Months 2–3 for bias monitoring and XAI implementation, Months 4–5 for audit trail infrastructure and documentation, Month 6 for staff training and process integration. Budget $50,000–$200,000 depending on the number of high-risk AI systems.
