Why Must AI Governance Be Continuous?
Compliance for an AI system does not end with its classification, deployment, or initial assessment. Models, data, providers, internal processes, and contexts of use can change over time, potentially altering the associated risks and applicable obligations.
A continuous governance model makes it possible to detect these changes, periodically review the controls in place, and keep the necessary evidence up to date to support risk management, Human Oversight, and regulatory compliance.
How Does Continuous AI Governance Work?
1. Governance Model Definition
We establish responsibilities, functions, controls, review cycles, and escalation mechanisms for the continuous oversight of AI systems.
2. Risk and Change Monitoring
We periodically review changes to models, data, providers, intended purposes, users, and contexts of use that may affect the system’s risk profile.
3. Documentation Updates and Traceability
We keep evidence, records, technical documentation, and governance decisions associated with the AI system lifecycle up to date.
4. Integration into Corporate Processes
We embed AI governance into existing Risk, Compliance, Legal, IT, Security, Quality, and supplier management processes.
5. Regulatory Monitoring and Continuous Improvement
We assess new regulatory requirements, identified deviations, and corrective actions to keep the system aligned with the applicable regulatory framework.
Deliverables
1. Continuous AI Governance & Integration Plan – vAIO
An Aicertia document defining the oversight model, assigned responsibilities, controls, review frequency, escalation criteria, and governance mechanisms for the AI system.
2. Living Monitoring & Compliance Matrix
A structured record of risks, evidence, relevant changes, identified deviations, corrective actions, responsible stakeholders, and next steps.
3. System Review & Evolution Record
Documented traceability of completed reviews and changes that may affect the system’s regulatory, technical, or operational profile.
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