Control Framework

What this means in practice

For AI governance, control frameworks define the specific controls needed to ensure model accuracy, data quality, fairness, security, transparency, and human oversight across the AI lifecycle. Controls may be preventive (stopping problems before they occur), detective (identifying problems that have occurred), or corrective (addressing problems after detection). For organizations, a well-designed control framework is the operational manifestation of governance policy, translating principles into actionable, measurable practices. In COMPEL, control frameworks are designed during the Model stage under the Governance pillar and operationalized during Produce, with effectiveness assessed during Evaluate.

Why it matters

A well-designed control framework is the operational manifestation of governance policy, translating abstract principles into actionable, measurable practices. Without it, organizations have governance policies that sound good on paper but do not change how AI systems are actually built, deployed, and monitored. Controls may be preventive, detective, or corrective, and together they provide the defense-in-depth that regulators and auditors expect to see.

How COMPEL uses it

Control frameworks are designed during the Model stage under the Governance pillar and operationalized during Produce. The Calibrate stage assesses existing control maturity across domains. Preventive controls are implemented before deployment, detective controls monitor production systems, and corrective controls address issues after detection. The Evaluate stage measures control effectiveness, and the Learn stage refines the control framework based on incident patterns and audit findings.

Related Terms

Other glossary terms mentioned in this entry's definition and context.

• Accuracy
• AI Governance
• Calibrate Stage
• Data Quality
• Defense in Depth
• Evaluate Stage
• Fairness
• Governance Control
• Human Oversight
• Learn Stage
• Model Stage
• Transparency